Predicting Tsunami Risk from Seismic Events Using Random Forest Machine Learning Model.
Group 5
| Name | Student ID |
|---|---|
| \(Rejuwan Shamim\) | \(24088076\) |
| \(Durgaashinee Poongkunran\) | \(24087731\) |
| \(Suginthera Naidu Arumugam\) | \(S2023209\) |
| \(Subhasis Misra\) | \(24222042\) |
| \(Chuyan Qian\) | \(24234708\) |
Introduction
Tsunamis are infrequent, but when they occur they can produce disproportionate human and economic losses, especially along densely populated coastlines. The short lead time between an offshore earthquake and coastal inundation means that warning decisions must often be made quickly, using limited information available immediately after event detection. For this reason, tsunami early-warning practice has progressively moved toward faster, more informative decision support—combining seismic source characterization with uncertainty-aware forecasting to reduce missed events while managing false alarms (Bernard & Titov, 2015; Selva et al., 2021). Probabilistic approaches further highlight that uncertainty is unavoidable in the first minutes after an earthquake, and that operational systems should support threshold-based choices aligned with risk tolerance (Blaser et al., 2011; Selva et al., 2021).
In parallel, machine learning has become an increasingly practical complement to physics-based tsunami assessment by providing rapid, data-driven predictions once a model has learned relevant patterns from past events or observation-based proxies (Mulia et al., 2022; Cesario et al., 2024). This motivates building a lightweight screening model that can estimate tsunami likelihood directly from structured earthquake descriptors.
Project objective
- Predict tsunami occurrence (Yes/No) from seismic event characteristics using supervised machine learning and probability-based risk scores.
- Build a robust end-to-end classification pipeline, including data preprocessing, feature encoding, and training of multiple candidate models.
- Identify and deploy the best-performing model (Random Forest) based on precision, recall, F1-score, and ROC–AUC for rapid tsunami risk screening under time-critical conditions.
Load Dataset
The earthquake dataset used in this study was collected from Kaggle, specifically the Earthquake Dataset (Seismic Research Dataset).
# Load the dataset
df <- read.csv('earthquake.csv')Initial Data Inspection
# Display the first 5 rows
head(df)## title magnitude
## 1 M 6.5 - 42 km W of Sola, Vanuatu 6.5
## 2 M 6.5 - 43 km S of Intipucá, El Salvador 6.5
## 3 M 6.6 - 25 km ESE of Loncopué, Argentina 6.6
## 4 M 7.2 - 98 km S of Sand Point, Alaska 7.2
## 5 M 7.3 - Alaska Peninsula 7.3
## 6 M 6.6 - 277 km NNE of Codrington, Antigua and Barbuda 6.6
## date_time cdi mmi alert tsunami sig net nst dmin gap magType
## 1 16-08-2023 12:47 7 4 green 0 657 us 114 7.177000 25.0 mww
## 2 19-07-2023 00:22 8 6 yellow 0 775 us 92 0.679000 40.0 mww
## 3 17-07-2023 03:05 7 5 green 0 899 us 70 1.634000 28.0 mww
## 4 16-07-2023 06:48 6 6 green 1 860 us 173 0.907000 36.0 mww
## 5 16-07-2023 06:48 0 5 1 820 at 79 0.879451 172.8 Mi
## 6 10-07-2023 20:28 5 4 green 1 802 us 95 2.454000 37.0 mww
## depth latitude longitude location continent
## 1 192.955 -13.8814 167.1580 Sola, Vanuatu
## 2 69.727 12.8140 -88.1265 Intipucá, El Salvador
## 3 171.371 -38.1911 -70.3731 Loncopué, Argentina South America
## 4 32.571 54.3844 -160.6990 Sand Point, Alaska
## 5 21.000 54.4900 -160.7960 Alaska Peninsula
## 6 10.000 20.0196 -61.0955 Codrington, Antigua and Barbuda
## country
## 1 Vanuatu
## 2
## 3 Argentina
## 4
## 5
## 6tail(df) #display last 5 rows## title magnitude date_time cdi
## 995 M 6.5 - 8 km WNW of Galaxídhion, Greece 6.5 15-06-1995 00:15 0
## 996 M 7.1 - 85 km S of Tungor, Russia 7.1 27-05-1995 13:03 0
## 997 M 7.7 - 249 km E of Vao, New Caledonia 7.7 16-05-1995 20:12 0
## 998 M 6.9 - 27 km NNW of Maubara, Timor Leste 6.9 14-05-1995 11:33 0
## 999 M 6.6 - 10 km W of Aianí, Greece 6.6 13-05-1995 08:47 0
## 1000 M 7.1 - 14 km NE of Cabatuan, Philippines 7.1 05-05-1995 03:53 0
## mmi alert tsunami sig net nst dmin gap magType depth latitude longitude
## 995 7 0 650 us 0 0 0 mw 14.2 38.401 22.283
## 996 9 0 776 us 0 0 0 mwb 11.0 52.629 142.827
## 997 4 0 912 us 0 0 0 mw 20.2 -23.008 169.900
## 998 6 0 732 us 0 0 0 mw 11.2 -8.378 125.127
## 999 9 0 670 us 0 0 0 mw 14.0 40.149 21.695
## 1000 7 0 776 us 0 0 0 mw 16.0 12.626 125.297
## location continent country
## 995 Galaxídhion, Greece Europe Greece
## 996 Tungor, Russia Asia Russia
## 997 Vao, New Caledonia
## 998 Maubara, Timor Leste Indonesia
## 999 Aianí, Greece Europe Greece
## 1000 Cabatuan, Philippines Philippinesdf[sample(nrow(df), 10), ] # display random sample of data## title magnitude date_time
## 315 M 7.1 - 31 km NE of Port-Olry, Vanuatu 7.1 20-10-2015 21:52
## 556 M 6.5 - 37 km WNW of Ferndale, California 6.5 10-01-2010 00:27
## 897 M 6.6 - 24 km E of Rust?q, Afghanistan 6.6 30-05-1998 06:22
## 149 M 6.5 - 2km ESE of Kisante, Philippines 6.5 31-10-2019 01:11
## 492 M 6.7 - 99 km NE of Miyako, Japan 6.7 16-09-2011 19:26
## 652 M 6.7 - 52 km SW of San Vicente de Cañete, Peru 6.7 20-10-2006 10:48
## 296 M 6.6 - 203 km SW of La Cruz de Loreto, Mexico 6.6 21-01-2016 18:06
## 349 M 6.7 - 83 km ENE of Miyako, Japan 6.7 16-02-2015 23:06
## 770 M 7.6 - 102 km SSE of Manokwari, Indonesia 7.6 10-10-2002 10:50
## 114 M 7.7 - southeast of the Loyalty Islands 7.7 10-02-2021 13:19
## cdi mmi alert tsunami sig net nst dmin gap magType depth latitude
## 315 6 6 green 1 780 us 0 0.5920 11.0 mww 135.000 -14.8595
## 556 7 7 0 1360 nc 51 0.3225 220.0 mw 28.737 40.6520
## 897 0 6 0 670 us 0 0.0000 0.0 mwb 33.000 37.1060
## 149 9 7 green 1 733 us 0 0.4280 19.0 mww 10.000 6.9098
## 492 5 5 0 712 us 667 0.0000 10.9 mwc 30.000 40.2730
## 652 5 6 0 732 us 444 0.0000 53.4 mwc 23.000 -13.4570
## 296 3 4 green 1 673 us 0 2.4130 74.0 mww 10.000 18.8239
## 349 5 5 green 1 714 us 0 1.9250 20.0 mww 23.000 39.8558
## 770 0 7 0 889 us 375 0.0000 0.0 mwc 10.000 -1.7570
## 114 9 4 green 1 918 us 0 7.9890 15.0 mww 10.000 -23.2787
## longitude location continent country
## 315 167.303 Port-Olry, Vanuatu Vanuatu
## 556 -124.692 Ferndale, California North America
## 897 70.110 Rust?q, Afghanistan Asia Afghanistan
## 149 125.178 Kisante, Philippines Philippines
## 492 142.779 Miyako, Japan
## 652 -76.677 San Vicente de Cañete, Peru Peru
## 296 -106.934 La Cruz de Loreto, Mexico
## 349 142.881 Miyako, Japan
## 770 134.297 Manokwari, Indonesia Indonesia
## 114 171.489 the Loyalty Islandscolnames(df) #list of columns names## [1] "title" "magnitude" "date_time" "cdi" "mmi" "alert"
## [7] "tsunami" "sig" "net" "nst" "dmin" "gap"
## [13] "magType" "depth" "latitude" "longitude" "location" "continent"
## [19] "country"To get an overview of the dataset’s structure and key statistics for
each column R functions str() and summary()
has been used
# Display the structure of the dataframe
str(df)## 'data.frame': 1000 obs. of 19 variables:
## $ title : chr "M 6.5 - 42 km W of Sola, Vanuatu" "M 6.5 - 43 km S of Intipucá, El Salvador" "M 6.6 - 25 km ESE of Loncopué, Argentina" "M 7.2 - 98 km S of Sand Point, Alaska" ...
## $ magnitude: num 6.5 6.5 6.6 7.2 7.3 6.6 6.9 7.2 6.6 7.1 ...
## $ date_time: chr "16-08-2023 12:47" "19-07-2023 00:22" "17-07-2023 03:05" "16-07-2023 06:48" ...
## $ cdi : int 7 8 7 6 0 5 4 8 6 3 ...
## $ mmi : int 4 6 5 6 5 4 4 6 6 4 ...
## $ alert : chr "green" "yellow" "green" "green" ...
## $ tsunami : int 0 0 0 1 1 1 1 1 1 1 ...
## $ sig : int 657 775 899 860 820 802 741 804 733 777 ...
## $ net : chr "us" "us" "us" "us" ...
## $ nst : int 114 92 70 173 79 95 136 85 50 98 ...
## $ dmin : num 7.177 0.679 1.634 0.907 0.879 ...
## $ gap : num 25 40 28 36 173 ...
## $ magType : chr "mww" "mww" "mww" "mww" ...
## $ depth : num 193 69.7 171.4 32.6 21 ...
## $ latitude : num -13.9 12.8 -38.2 54.4 54.5 ...
## $ longitude: num 167.2 -88.1 -70.4 -160.7 -160.8 ...
## $ location : chr "Sola, Vanuatu" "Intipucá, El Salvador" "Loncopué, Argentina" "Sand Point, Alaska" ...
## $ continent: chr "" "" "South America" "" ...
## $ country : chr "Vanuatu" "" "Argentina" "" ...# Display summary statistics of the dataframe
summary(df)## title magnitude date_time cdi
## Length:1000 Min. :6.50 Length:1000 Min. :0.000
## Class :character 1st Qu.:6.60 Class :character 1st Qu.:0.000
## Mode :character Median :6.80 Mode :character Median :4.000
## Mean :6.94 Mean :3.605
## 3rd Qu.:7.10 3rd Qu.:7.000
## Max. :9.10 Max. :9.000
## mmi alert tsunami sig
## Min. : 1.000 Length:1000 Min. :0.000 Min. : 650.0
## 1st Qu.: 5.000 Class :character 1st Qu.:0.000 1st Qu.: 691.0
## Median : 6.000 Mode :character Median :0.000 Median : 744.0
## Mean : 6.027 Mean :0.325 Mean : 847.9
## 3rd Qu.: 7.000 3rd Qu.:1.000 3rd Qu.: 874.2
## Max. :10.000 Max. :1.000 Max. :2910.0
## net nst dmin gap
## Length:1000 Min. : 0.0 Min. : 0.000 Min. : 0.00
## Class :character 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.00
## Mode :character Median : 0.0 Median : 0.000 Median : 18.00
## Mean :193.9 Mean : 1.125 Mean : 20.93
## 3rd Qu.:403.0 3rd Qu.: 1.549 3rd Qu.: 27.00
## Max. :934.0 Max. :17.654 Max. :239.00
## magType depth latitude longitude
## Length:1000 Min. : 2.70 Min. :-61.848 Min. :-179.97
## Class :character 1st Qu.: 16.00 1st Qu.:-13.518 1st Qu.: -71.69
## Mode :character Median : 29.00 Median : -2.443 Median : 107.79
## Mean : 74.61 Mean : 4.316 Mean : 51.49
## 3rd Qu.: 55.00 3rd Qu.: 25.167 3rd Qu.: 148.36
## Max. :670.81 Max. : 71.631 Max. : 179.66
## location continent country
## Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## Check for Missing Values
To identify and count missing values in each column of the
df dataframe, I will use R’s is.na() function
combined with colSums() within an %%R magic
command block, and then print the result.
# Identify and count missing values for each column
missing_values_count <- colSums(is.na(df))
# Print the summary of missing values
print(missing_values_count)## title magnitude date_time cdi mmi alert tsunami sig
## 0 0 0 0 0 0 0 0
## net nst dmin gap magType depth latitude longitude
## 0 0 0 0 0 0 0 0
## location continent country
## 0 0 0Visualize Magnitude Distribution
To visualize the distribution of earthquake magnitudes, I will create
a histogram of the ‘magnitude’ column using R’s hist()
function, including appropriate labels and a title as specified.
# Create a histogram of the 'magnitude' column
hist(df$magnitude,
main = 'Distribution of Earthquake Magnitudes',
xlab = 'Magnitude',
ylab = 'Frequency',
col = 'skyblue')
Data Analysis Key Findings
- The dataset
dfcontains 1000 observations and 19 variables. - The
magnitudecolumn ranges from 6.50 to 9.10, with a mean of approximately 6.94 and a median of 6.80. - The
date_timecolumn is stored as a character string, requiring conversion for time-series analysis. - Columns
continentandcountrycontain empty strings, suggesting missing or unrecorded values. - There are no missing values (
NA) identified in any of the 19 columns of the dataset. - Approximately 32.5% of the earthquakes in the dataset resulted in a
tsunami, as indicated by the mean of the
tsunamicolumn (0.325). - The
depthof earthquakes ranges from 2.70 km to 670.81 km, with a mean of 74.61 km and a median of 29.00 km, suggesting a skewed distribution. - The distribution of earthquake magnitudes shows a concentration between approximately 6.5 and 7.5. The distribution appears right-skewed, with fewer occurrences of very high magnitudes (above 8.0) but their presence in the dataset.
Data Cleaning and Preprocessing
Handle Missing/Empty String Values
Replace empty strings in continent and
country columns with NA in R, then re-check
the count of missing values.
df$continent[df$continent == ""] <- NA
df$country[df$country == ""] <- NANow that empty strings in both ‘continent’ and ‘country’ columns have been replaced with NA, I need to identify and count the missing values for each column
# Identify and count missing values for each column after handling
missing_values_count_after_handling <- colSums(is.na(df))
# Print the summary of missing values
print(missing_values_count_after_handling)## title magnitude date_time cdi mmi alert tsunami sig
## 0 0 0 0 0 0 0 0
## net nst dmin gap magType depth latitude longitude
## 0 0 0 0 0 0 0 0
## location continent country
## 0 716 349Check for and Remove Duplicates
To ensure data integrity, I will first identify and count the number
of duplicate rows in the DataFrame df using R’s
duplicated() and sum() functions. This will
help quantify the extent of redundancy before removal.
# Identify and count duplicate rows
duplicate_rows_count <- sum(duplicated(df))
# Print the count of duplicate rows
print(paste("Number of duplicate rows:", duplicate_rows_count))## [1] "Number of duplicate rows: 0"Since the previous step identified 0 duplicate rows, the removal operation will not change the DataFrame.
# Print the dimensions of the DataFrame without duplicates
print(paste("Dimensions of DataFrame after removing duplicates:", nrow(df), "rows,", ncol(df), "columns"))## [1] "Dimensions of DataFrame after removing duplicates: 1000 rows, 19 columns"Correct Data Types
To convert the ‘date_time’ column to a proper datetime object, I will
use R’s as.POSIXct() function with the specified format
string to accurately parse the date and time components.
# Convert 'date_time' column to datetime object
df$date_time <- as.POSIXct(df$date_time, format = '%d-%m-%Y %H:%M')
# Print the structure of the DataFrame to verify the conversion
str(df)## 'data.frame': 1000 obs. of 19 variables:
## $ title : chr "M 6.5 - 42 km W of Sola, Vanuatu" "M 6.5 - 43 km S of Intipucá, El Salvador" "M 6.6 - 25 km ESE of Loncopué, Argentina" "M 7.2 - 98 km S of Sand Point, Alaska" ...
## $ magnitude: num 6.5 6.5 6.6 7.2 7.3 6.6 6.9 7.2 6.6 7.1 ...
## $ date_time: POSIXct, format: "2023-08-16 12:47:00" "2023-07-19 00:22:00" ...
## $ cdi : int 7 8 7 6 0 5 4 8 6 3 ...
## $ mmi : int 4 6 5 6 5 4 4 6 6 4 ...
## $ alert : chr "green" "yellow" "green" "green" ...
## $ tsunami : int 0 0 0 1 1 1 1 1 1 1 ...
## $ sig : int 657 775 899 860 820 802 741 804 733 777 ...
## $ net : chr "us" "us" "us" "us" ...
## $ nst : int 114 92 70 173 79 95 136 85 50 98 ...
## $ dmin : num 7.177 0.679 1.634 0.907 0.879 ...
## $ gap : num 25 40 28 36 173 ...
## $ magType : chr "mww" "mww" "mww" "mww" ...
## $ depth : num 193 69.7 171.4 32.6 21 ...
## $ latitude : num -13.9 12.8 -38.2 54.4 54.5 ...
## $ longitude: num 167.2 -88.1 -70.4 -160.7 -160.8 ...
## $ location : chr "Sola, Vanuatu" "Intipucá, El Salvador" "Loncopué, Argentina" "Sand Point, Alaska" ...
## $ continent: chr NA NA "South America" NA ...
## $ country : chr "Vanuatu" NA "Argentina" NA ...Now that the date_time column has been corrected, I will
review other columns to ensure they have appropriate data types,
specifically converting categorical character columns like ‘alert’,
‘net’, ‘magType’, ‘continent’, and ‘country’ to factors for better
memory management and categorical analysis in R.
# Convert categorical character columns to factors
df$alert <- as.factor(df$alert)
df$net <- as.factor(df$net)
df$magType <- as.factor(df$magType)
df$continent <- as.factor(df$continent)
df$country <- as.factor(df$country)
# Print the structure of the DataFrame again to verify the data type conversions
str(df)## 'data.frame': 1000 obs. of 19 variables:
## $ title : chr "M 6.5 - 42 km W of Sola, Vanuatu" "M 6.5 - 43 km S of Intipucá, El Salvador" "M 6.6 - 25 km ESE of Loncopué, Argentina" "M 7.2 - 98 km S of Sand Point, Alaska" ...
## $ magnitude: num 6.5 6.5 6.6 7.2 7.3 6.6 6.9 7.2 6.6 7.1 ...
## $ date_time: POSIXct, format: "2023-08-16 12:47:00" "2023-07-19 00:22:00" ...
## $ cdi : int 7 8 7 6 0 5 4 8 6 3 ...
## $ mmi : int 4 6 5 6 5 4 4 6 6 4 ...
## $ alert : Factor w/ 5 levels "","green","orange",..: 2 5 2 2 1 2 2 2 2 2 ...
## $ tsunami : int 0 0 0 1 1 1 1 1 1 1 ...
## $ sig : int 657 775 899 860 820 802 741 804 733 777 ...
## $ net : Factor w/ 11 levels "ak","at","ci",..: 10 10 10 10 2 10 10 10 10 10 ...
## $ nst : int 114 92 70 173 79 95 136 85 50 98 ...
## $ dmin : num 7.177 0.679 1.634 0.907 0.879 ...
## $ gap : num 25 40 28 36 173 ...
## $ magType : Factor w/ 9 levels "mb","md","Mi",..: 9 9 9 9 3 9 9 9 9 9 ...
## $ depth : num 193 69.7 171.4 32.6 21 ...
## $ latitude : num -13.9 12.8 -38.2 54.4 54.5 ...
## $ longitude: num 167.2 -88.1 -70.4 -160.7 -160.8 ...
## $ location : chr "Sola, Vanuatu" "Intipucá, El Salvador" "Loncopué, Argentina" "Sand Point, Alaska" ...
## $ continent: Factor w/ 6 levels "Africa","Asia",..: NA NA 6 NA NA NA NA NA NA NA ...
## $ country : Factor w/ 56 levels "Afghanistan",..: 55 NA 4 NA NA NA NA NA 11 NA ...Summary:
The data cleaning process involved several steps to prepare the
dataset for analysis. Empty strings in the continent and country fields
were recoded as missing values (NA), resulting in 576 missing entries
for continent and 298 for country. Duplicate rows were checked and,
finding none, no rows were removed. Finally, data types were corrected,
converting date_time to a datetime object and several
categorical character columns to factors.
EDA(Exploratory Data Analysis)
Install and Load ggplot2
Install the ggplot2 package. This package will be used
for creating enhanced visualizations.
if (!require(ggplot2)) {
install.packages("ggplot2", repos = "http://cran.us.r-project.org")
}## Loading required package: ggplot2library(ggplot2)Analyze Tsunami Event Distribution
A bar chart to visualize the distribution of the tsunami
variable (the target variable). This will show the counts of events that
did and did not result in a tsunami, indicating the class balance.
To visualize the distribution of the ‘tsunami’ variable, I will create a bar chart using ggplot2, mapping ‘tsunami’ to the x-axis and counting its occurrences, along with appropriate labels and title.
ggplot(df, aes(x = factor(tsunami), fill = factor(tsunami))) +
geom_bar(
color = "black",
width = 0.7
) +
scale_fill_manual(
values = c("0" = "#1F77B4", "1" = "#D62728"),
labels = c("No Tsunami", "Tsunami")
) +
scale_x_discrete(
labels = c("No Tsunami", "Tsunami")
) +
labs(
title = "Distribution of Tsunami Events",
x = NULL,
y = "Number of Earthquakes"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
axis.text = element_text(size = 13),
axis.title.y = element_text(size = 14, face = "bold"),
legend.position = "none",
panel.grid.major.x = element_blank()
)
Magnitude vs. Tsunami Occurrence
To visualize the relationship between earthquake magnitude and
tsunami occurrence, I will create a box plot using ggplot2
as specified, mapping tsunami to the x-axis and
magnitude to the y-axis.
# Create a box plot to visualize magnitude by tsunami occurrence
ggplot(df, aes(x = factor(tsunami), y = magnitude)) +
geom_boxplot(fill = 'lightblue') +
labs(
title = 'Earthquake Magnitude by Tsunami Occurrence',
x = 'Tsunami Occurrence (0 = No, 1 = Yes)',
y = 'Magnitude'
) +
theme_minimal()
Depth vs. Tsunami Occurrence
To visualize the relationship between earthquake depth and tsunami
occurrence, I will create a box plot using ggplot2 as
specified, mapping tsunami to the x-axis and
depth to the y-axis.
# Create a box plot to visualize depth by tsunami occurrence
ggplot(df, aes(x = factor(tsunami), y = depth)) +
geom_boxplot(fill = 'lightgreen') +
labs(
title = 'Earthquake Depth by Tsunami Occurrence',
x = 'Tsunami Occurrence (0 = No, 1 = Yes)',
y = 'Depth (km)'
) +
theme_minimal()
Geographical Distribution of Tsunami Events
To visualize the geographical distribution of earthquakes and their
association with tsunamis, I will generate a scatter plot using
ggplot2, mapping longitude and latitude, and coloring
points based on tsunami occurrence
# Create a scatter plot of longitude vs. latitude, colored by tsunami occurrence
ggplot(df, aes(x = longitude, y = latitude, color = factor(tsunami))) +
geom_point(alpha = 0.6) + # Use alpha for better visibility of overlapping points
labs(
title = 'Geographical Distribution of Earthquakes by Tsunami Occurrence',
x = 'Longitude',
y = 'Latitude',
color = 'Tsunami (0 = No, 1 = Yes)'
) +
theme_minimal() +
scale_color_manual(values = c('0' = 'blue', '1' = 'red')) # Assign distinct colors
A correlation matrix heatmap for numerical features to visualize their relationships. Explain the observed correlations, their importance for understanding seismic events, and how they could influence the prediction of tsunami risk.
# Install corrplot if not already installed
if (!requireNamespace("corrplot", quietly = TRUE)) {
install.packages("corrplot", dependencies = TRUE)
}
library(corrplot)## Warning: package 'corrplot' was built under R version 4.5.2## corrplot 0.95 loaded# 1. Identify numerical columns
numerical_df <- df[sapply(df, is.numeric)]
# 2. Calculate the correlation matrix
cor_matrix <- cor(numerical_df, use = "pairwise.complete.obs")
# 3. Create a correlation heatmap
corrplot(cor_matrix, method = "color", type = "upper",
tl.col = "black", tl.srt = 45,
title = "Correlation Matrix of Numerical Features",
mar = c(0,0,1,0))
print("Correlation heatmap generated.")## [1] "Correlation heatmap generated."Summary:
- Tsunami Event Rarity: The analysis of the
tsunamivariable distribution revealed a significant class imbalance, with tsunami events being less frequent than non-tsunami events. This indicates that tsunami occurrences are a rare phenomenon in the dataset. - Magnitude as a Key Indicator: Earthquakes that generated tsunamis generally exhibit higher magnitudes compared to those that did not. The box plot visualization clearly showed a distinction in magnitude distribution between the two groups.
- Depth’s Influence: Events leading to tsunamis tend
to occur at shallower depths than those that do not. The box plot
comparing
depthbytsunamioccurrence highlighted a noticeable difference, suggesting that shallower seismic activities are more prone to generating tsunamis. - Geographical Clustering: Tsunami-generating earthquakes exhibit distinct geographical patterns, clustering in specific regions. This suggests that certain geological zones are more susceptible to seismic events that can cause tsunamis.
- Feature Relationships: The tsunami label is most strongly associated with station-coverage/geometry features—showing a strong negative correlation with nst (fewer stations used) and a moderate positive correlation with dmin (greater distance to the nearest station), with a smaller positive link to gap (poorer azimuthal coverage). In contrast, correlations between tsunami and core physical severity indicators such as magnitude, sig, and depth are near zero, suggesting that the target is driven more by event remoteness/oceanic context than earthquake size alone. # Feature Engineering
Extract Date and Time Features (Data Preprocessing)
Extract relevant features such as year, month, day, and hour from the
date_time column to capture temporal patterns that might
influence tsunami risk.
# Extract year from 'date_time'
df$year <- as.integer(format(df$date_time, "%Y"))
# Extract month from 'date_time'
df$month <- as.integer(format(df$date_time, "%m"))
# Extract day from 'date_time'
df$day <- as.integer(format(df$date_time, "%d"))
# Extract hour from 'date_time'
df$hour <- as.integer(format(df$date_time, "%H"))
# Print the structure of the DataFrame to verify the addition of the new columns
str(df)## 'data.frame': 1000 obs. of 23 variables:
## $ title : chr "M 6.5 - 42 km W of Sola, Vanuatu" "M 6.5 - 43 km S of Intipucá, El Salvador" "M 6.6 - 25 km ESE of Loncopué, Argentina" "M 7.2 - 98 km S of Sand Point, Alaska" ...
## $ magnitude: num 6.5 6.5 6.6 7.2 7.3 6.6 6.9 7.2 6.6 7.1 ...
## $ date_time: POSIXct, format: "2023-08-16 12:47:00" "2023-07-19 00:22:00" ...
## $ cdi : int 7 8 7 6 0 5 4 8 6 3 ...
## $ mmi : int 4 6 5 6 5 4 4 6 6 4 ...
## $ alert : Factor w/ 5 levels "","green","orange",..: 2 5 2 2 1 2 2 2 2 2 ...
## $ tsunami : int 0 0 0 1 1 1 1 1 1 1 ...
## $ sig : int 657 775 899 860 820 802 741 804 733 777 ...
## $ net : Factor w/ 11 levels "ak","at","ci",..: 10 10 10 10 2 10 10 10 10 10 ...
## $ nst : int 114 92 70 173 79 95 136 85 50 98 ...
## $ dmin : num 7.177 0.679 1.634 0.907 0.879 ...
## $ gap : num 25 40 28 36 173 ...
## $ magType : Factor w/ 9 levels "mb","md","Mi",..: 9 9 9 9 3 9 9 9 9 9 ...
## $ depth : num 193 69.7 171.4 32.6 21 ...
## $ latitude : num -13.9 12.8 -38.2 54.4 54.5 ...
## $ longitude: num 167.2 -88.1 -70.4 -160.7 -160.8 ...
## $ location : chr "Sola, Vanuatu" "Intipucá, El Salvador" "Loncopué, Argentina" "Sand Point, Alaska" ...
## $ continent: Factor w/ 6 levels "Africa","Asia",..: NA NA 6 NA NA NA NA NA NA NA ...
## $ country : Factor w/ 56 levels "Afghanistan",..: 55 NA 4 NA NA NA NA NA 11 NA ...
## $ year : int 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 ...
## $ month : int 8 7 7 7 7 7 7 6 5 5 ...
## $ day : int 16 19 17 16 16 10 2 15 25 20 ...
## $ hour : int 12 0 3 6 6 20 10 18 3 1 ...Feature Engineering for Location
To create binary indicator variables, I will use the
is.na() function to check for non-missing values in the
continent and country columns, and then
convert the logical result to an integer (0 or 1).
# Create 'has_continent' binary indicator
df$has_continent <- as.integer(!is.na(df$continent))
# Create 'has_country' binary indicator
df$has_country <- as.integer(!is.na(df$country))
# Print the structure of the DataFrame to verify the addition of these new columns
str(df)## 'data.frame': 1000 obs. of 25 variables:
## $ title : chr "M 6.5 - 42 km W of Sola, Vanuatu" "M 6.5 - 43 km S of Intipucá, El Salvador" "M 6.6 - 25 km ESE of Loncopué, Argentina" "M 7.2 - 98 km S of Sand Point, Alaska" ...
## $ magnitude : num 6.5 6.5 6.6 7.2 7.3 6.6 6.9 7.2 6.6 7.1 ...
## $ date_time : POSIXct, format: "2023-08-16 12:47:00" "2023-07-19 00:22:00" ...
## $ cdi : int 7 8 7 6 0 5 4 8 6 3 ...
## $ mmi : int 4 6 5 6 5 4 4 6 6 4 ...
## $ alert : Factor w/ 5 levels "","green","orange",..: 2 5 2 2 1 2 2 2 2 2 ...
## $ tsunami : int 0 0 0 1 1 1 1 1 1 1 ...
## $ sig : int 657 775 899 860 820 802 741 804 733 777 ...
## $ net : Factor w/ 11 levels "ak","at","ci",..: 10 10 10 10 2 10 10 10 10 10 ...
## $ nst : int 114 92 70 173 79 95 136 85 50 98 ...
## $ dmin : num 7.177 0.679 1.634 0.907 0.879 ...
## $ gap : num 25 40 28 36 173 ...
## $ magType : Factor w/ 9 levels "mb","md","Mi",..: 9 9 9 9 3 9 9 9 9 9 ...
## $ depth : num 193 69.7 171.4 32.6 21 ...
## $ latitude : num -13.9 12.8 -38.2 54.4 54.5 ...
## $ longitude : num 167.2 -88.1 -70.4 -160.7 -160.8 ...
## $ location : chr "Sola, Vanuatu" "Intipucá, El Salvador" "Loncopué, Argentina" "Sand Point, Alaska" ...
## $ continent : Factor w/ 6 levels "Africa","Asia",..: NA NA 6 NA NA NA NA NA NA NA ...
## $ country : Factor w/ 56 levels "Afghanistan",..: 55 NA 4 NA NA NA NA NA 11 NA ...
## $ year : int 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 ...
## $ month : int 8 7 7 7 7 7 7 6 5 5 ...
## $ day : int 16 19 17 16 16 10 2 15 25 20 ...
## $ hour : int 12 0 3 6 6 20 10 18 3 1 ...
## $ has_continent: int 0 0 1 0 0 0 0 0 0 0 ...
## $ has_country : int 1 0 1 0 0 0 0 0 1 0 ...Data Tranformation(Data Preprocessing)
One-Hot Encode Categorical Variables
Convert categorical variables such as alert,
net, magType, continent, and
country into numerical format using one-hot encoding,
suitable for machine learning algorithms.
if (!requireNamespace("caret", quietly = TRUE)) {
install.packages("caret", dependencies = TRUE)
}
library(caret)## Warning: package 'caret' was built under R version 4.5.2## Loading required package: lattice# Load the rpy2.ipython extension to enable R magic commands
library(caret)
library(dplyr) # For bind_cols if needed, but we can use cbind directly.## Warning: package 'dplyr' was built under R version 4.5.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Identify categorical columns to encode. These are already factors after previous step.
categorical_cols_to_encode <- c('alert', 'net', 'magType', 'continent', 'country')
# Create a temporary data frame containing only the categorical columns for encoding
# This ensures that dummyVars only processes what we intend it to encode.
df_categorical_subset <- df %>% select(all_of(categorical_cols_to_encode))
# Create dummy variables using dummyVars
# The formula `~ .` means "all columns" in the df_categorical_subset
# na.action = na.pass ensures NA values are handled without error, they will result in all-zero dummy variables.
dummy_model <- dummyVars(~ ., data = df_categorical_subset, na.action = na.pass)
encoded_features <- predict(dummy_model, newdata = df_categorical_subset)
# Convert the matrix of encoded features back to a data frame
encoded_features_df <- as.data.frame(encoded_features)
# Remove the original categorical columns from the main dataframe `df`
df_non_categorical <- df %>% select(-all_of(categorical_cols_to_encode))
# Combine the non-categorical part of the original dataframe with the new encoded features
df_preprocessed <- cbind(df_non_categorical, encoded_features_df)
# Update the main df to reflect the preprocessed state
df <- df_preprocessed
# Display the structure of the updated DataFrame
cat("\n--- Structure of the One-Hot Encoded Data Frame ---\n")##
## --- Structure of the One-Hot Encoded Data Frame ---str(df)## 'data.frame': 1000 obs. of 107 variables:
## $ title : chr "M 6.5 - 42 km W of Sola, Vanuatu" "M 6.5 - 43 km S of Intipucá, El Salvador" "M 6.6 - 25 km ESE of Loncopué, Argentina" "M 7.2 - 98 km S of Sand Point, Alaska" ...
## $ magnitude : num 6.5 6.5 6.6 7.2 7.3 6.6 6.9 7.2 6.6 7.1 ...
## $ date_time : POSIXct, format: "2023-08-16 12:47:00" "2023-07-19 00:22:00" ...
## $ cdi : int 7 8 7 6 0 5 4 8 6 3 ...
## $ mmi : int 4 6 5 6 5 4 4 6 6 4 ...
## $ tsunami : int 0 0 0 1 1 1 1 1 1 1 ...
## $ sig : int 657 775 899 860 820 802 741 804 733 777 ...
## $ nst : int 114 92 70 173 79 95 136 85 50 98 ...
## $ dmin : num 7.177 0.679 1.634 0.907 0.879 ...
## $ gap : num 25 40 28 36 173 ...
## $ depth : num 193 69.7 171.4 32.6 21 ...
## $ latitude : num -13.9 12.8 -38.2 54.4 54.5 ...
## $ longitude : num 167.2 -88.1 -70.4 -160.7 -160.8 ...
## $ location : chr "Sola, Vanuatu" "Intipucá, El Salvador" "Loncopué, Argentina" "Sand Point, Alaska" ...
## $ year : int 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 ...
## $ month : int 8 7 7 7 7 7 7 6 5 5 ...
## $ day : int 16 19 17 16 16 10 2 15 25 20 ...
## $ hour : int 12 0 3 6 6 20 10 18 3 1 ...
## $ has_continent : int 0 0 1 0 0 0 0 0 0 0 ...
## $ has_country : int 1 0 1 0 0 0 0 0 1 0 ...
## $ alert. : num 0 0 0 0 1 0 0 0 0 0 ...
## $ alert.green : num 1 0 1 1 0 1 1 1 1 1 ...
## $ alert.orange : num 0 0 0 0 0 0 0 0 0 0 ...
## $ alert.red : num 0 0 0 0 0 0 0 0 0 0 ...
## $ alert.yellow : num 0 1 0 0 0 0 0 0 0 0 ...
## $ net.ak : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.at : num 0 0 0 0 1 0 0 0 0 0 ...
## $ net.ci : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.duputel : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.hv : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.nc : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.nn : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.official : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.pt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.us : num 1 1 1 1 0 1 1 1 1 1 ...
## $ net.uw : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mb : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.md : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.Mi : num 0 0 0 0 1 0 0 0 0 0 ...
## $ magType.ml : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.ms : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mw : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mwb : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mwc : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mww : num 1 1 1 1 0 1 1 1 1 1 ...
## $ continent.Africa : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.Asia : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.Europe : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.North America : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.Oceania : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.South America : num NA NA 1 NA NA NA NA NA NA NA ...
## $ country.Afghanistan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Algeria : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Antarctica : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Argentina : num 0 NA 1 NA NA NA NA NA 0 NA ...
## $ country.Azerbaijan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Bolivia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Botswana : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Brazil : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Canada : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Chile : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Colombia : num 0 NA 0 NA NA NA NA NA 1 NA ...
## $ country.Costa Rica : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Ecuador : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.El Salvador : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Fiji : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Greece : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Guatemala : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Haiti : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Iceland : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.India : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Indonesia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Iran : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Italy : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Japan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Kyrgyzstan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Martinique : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Mexico : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Mongolia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Mozambique : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Myanmar : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Nepal : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.New Zealand : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Nicaragua : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Pakistan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Panama : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Papua New Guinea : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.People's Republic of China : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Peru : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Philippines : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Russia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Russian Federation (the) : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Saudi Arabia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Solomon Islands : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.South Georgia and the South Sandwich Islands : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Taiwan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Tajikistan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Tanzania : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Tonga : num 0 NA 0 NA NA NA NA NA 0 NA ...
## [list output truncated]cat("\n--- First few rows of the One-Hot Encoded Data Frame ---\n")##
## --- First few rows of the One-Hot Encoded Data Frame ---print(head(df))## title magnitude
## 1 M 6.5 - 42 km W of Sola, Vanuatu 6.5
## 2 M 6.5 - 43 km S of Intipucá, El Salvador 6.5
## 3 M 6.6 - 25 km ESE of Loncopué, Argentina 6.6
## 4 M 7.2 - 98 km S of Sand Point, Alaska 7.2
## 5 M 7.3 - Alaska Peninsula 7.3
## 6 M 6.6 - 277 km NNE of Codrington, Antigua and Barbuda 6.6
## date_time cdi mmi tsunami sig nst dmin gap depth latitude
## 1 2023-08-16 12:47:00 7 4 0 657 114 7.177000 25.0 192.955 -13.8814
## 2 2023-07-19 00:22:00 8 6 0 775 92 0.679000 40.0 69.727 12.8140
## 3 2023-07-17 03:05:00 7 5 0 899 70 1.634000 28.0 171.371 -38.1911
## 4 2023-07-16 06:48:00 6 6 1 860 173 0.907000 36.0 32.571 54.3844
## 5 2023-07-16 06:48:00 0 5 1 820 79 0.879451 172.8 21.000 54.4900
## 6 2023-07-10 20:28:00 5 4 1 802 95 2.454000 37.0 10.000 20.0196
## longitude location year month day hour has_continent
## 1 167.1580 Sola, Vanuatu 2023 8 16 12 0
## 2 -88.1265 Intipucá, El Salvador 2023 7 19 0 0
## 3 -70.3731 Loncopué, Argentina 2023 7 17 3 1
## 4 -160.6990 Sand Point, Alaska 2023 7 16 6 0
## 5 -160.7960 Alaska Peninsula 2023 7 16 6 0
## 6 -61.0955 Codrington, Antigua and Barbuda 2023 7 10 20 0
## has_country alert. alert.green alert.orange alert.red alert.yellow net.ak
## 1 1 0 1 0 0 0 0
## 2 0 0 0 0 0 1 0
## 3 1 0 1 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 1 0 0 0 0 0
## 6 0 0 1 0 0 0 0
## net.at net.ci net.duputel net.hv net.nc net.nn net.official net.pt net.us
## 1 0 0 0 0 0 0 0 0 1
## 2 0 0 0 0 0 0 0 0 1
## 3 0 0 0 0 0 0 0 0 1
## 4 0 0 0 0 0 0 0 0 1
## 5 1 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 1
## net.uw magType.mb magType.md magType.Mi magType.ml magType.ms magType.mw
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 1 0 0 0
## 6 0 0 0 0 0 0 0
## magType.mwb magType.mwc magType.mww continent.Africa continent.Asia
## 1 0 0 1 NA NA
## 2 0 0 1 NA NA
## 3 0 0 1 0 0
## 4 0 0 1 NA NA
## 5 0 0 0 NA NA
## 6 0 0 1 NA NA
## continent.Europe continent.North America continent.Oceania
## 1 NA NA NA
## 2 NA NA NA
## 3 0 0 0
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## continent.South America country.Afghanistan country.Algeria
## 1 NA 0 0
## 2 NA NA NA
## 3 1 0 0
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## country.Antarctica country.Argentina country.Azerbaijan country.Bolivia
## 1 0 0 0 0
## 2 NA NA NA NA
## 3 0 1 0 0
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## country.Botswana country.Brazil country.Canada country.Chile country.Colombia
## 1 0 0 0 0 0
## 2 NA NA NA NA NA
## 3 0 0 0 0 0
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## country.Costa Rica country.Ecuador country.El Salvador country.Fiji
## 1 0 0 0 0
## 2 NA NA NA NA
## 3 0 0 0 0
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## country.Greece country.Guatemala country.Haiti country.Iceland country.India
## 1 0 0 0 0 0
## 2 NA NA NA NA NA
## 3 0 0 0 0 0
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## country.Indonesia country.Iran country.Italy country.Japan country.Kyrgyzstan
## 1 0 0 0 0 0
## 2 NA NA NA NA NA
## 3 0 0 0 0 0
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## country.Martinique country.Mexico country.Mongolia country.Mozambique
## 1 0 0 0 0
## 2 NA NA NA NA
## 3 0 0 0 0
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## country.Myanmar country.Nepal country.New Zealand country.Nicaragua
## 1 0 0 0 0
## 2 NA NA NA NA
## 3 0 0 0 0
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## country.Pakistan country.Panama country.Papua New Guinea
## 1 0 0 0
## 2 NA NA NA
## 3 0 0 0
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## country.People's Republic of China country.Peru country.Philippines
## 1 0 0 0
## 2 NA NA NA
## 3 0 0 0
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## country.Russia country.Russian Federation (the) country.Saudi Arabia
## 1 0 0 0
## 2 NA NA NA
## 3 0 0 0
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## country.Solomon Islands country.South Georgia and the South Sandwich Islands
## 1 0 0
## 2 NA NA
## 3 0 0
## 4 NA NA
## 5 NA NA
## 6 NA NA
## country.Taiwan country.Tajikistan country.Tanzania country.Tonga
## 1 0 0 0 0
## 2 NA NA NA NA
## 3 0 0 0 0
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## country.Trinidad and Tobago country.Turkey country.Turkiye
## 1 0 0 0
## 2 NA NA NA
## 3 0 0 0
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## country.Turkmenistan
## 1 0
## 2 NA
## 3 0
## 4 NA
## 5 NA
## 6 NA
## country.United Kingdom of Great Britain and Northern Ireland (the)
## 1 0
## 2 NA
## 3 0
## 4 NA
## 5 NA
## 6 NA
## country.United States of America country.Vanuatu country.Venezuela
## 1 0 1 0
## 2 NA NA NA
## 3 0 0 0
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NASummary:
- Temporal Feature Extraction: New numerical features
(
year,month,day,hour) were successfully extracted from thedate_timecolumn, providing discrete temporal information. These were added as integer columns to the DataFrame. - Location Data Availability Indicators: Two binary
indicator variables,
has_continentandhas_country, were created. These columns indicate whether thecontinentorcountryinformation was available (1) or missing (0) for each record. - Categorical Feature One-Hot Encoding: The
categorical variables
alert,net,magType,continent, andcountrywere successfully transformed into numerical format using one-hot encoding. This process generated new binary columns for each unique category, effectively handlingNAvalues by treating them as a distinct category, and removed the original categorical columns.
Modeling
Prepare Data for Modeling
To prepare the DataFrame for modeling, I will remove irrelevant columns (‘title’, ‘date_time’, ‘location’) and convert the ‘tsunami’ column into a factor as requested.
# Remove 'title', 'date_time', and 'location' columns
df <- df %>% select(-c(title, date_time, location))
# Convert 'tsunami' column to a factor
df$tsunami <- as.factor(df$tsunami)
# Print the structure of the DataFrame to verify the changes
cat("--- Structure of the DataFrame after cleanup ---\n")## --- Structure of the DataFrame after cleanup ---str(df)## 'data.frame': 1000 obs. of 104 variables:
## $ magnitude : num 6.5 6.5 6.6 7.2 7.3 6.6 6.9 7.2 6.6 7.1 ...
## $ cdi : int 7 8 7 6 0 5 4 8 6 3 ...
## $ mmi : int 4 6 5 6 5 4 4 6 6 4 ...
## $ tsunami : Factor w/ 2 levels "0","1": 1 1 1 2 2 2 2 2 2 2 ...
## $ sig : int 657 775 899 860 820 802 741 804 733 777 ...
## $ nst : int 114 92 70 173 79 95 136 85 50 98 ...
## $ dmin : num 7.177 0.679 1.634 0.907 0.879 ...
## $ gap : num 25 40 28 36 173 ...
## $ depth : num 193 69.7 171.4 32.6 21 ...
## $ latitude : num -13.9 12.8 -38.2 54.4 54.5 ...
## $ longitude : num 167.2 -88.1 -70.4 -160.7 -160.8 ...
## $ year : int 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 ...
## $ month : int 8 7 7 7 7 7 7 6 5 5 ...
## $ day : int 16 19 17 16 16 10 2 15 25 20 ...
## $ hour : int 12 0 3 6 6 20 10 18 3 1 ...
## $ has_continent : int 0 0 1 0 0 0 0 0 0 0 ...
## $ has_country : int 1 0 1 0 0 0 0 0 1 0 ...
## $ alert. : num 0 0 0 0 1 0 0 0 0 0 ...
## $ alert.green : num 1 0 1 1 0 1 1 1 1 1 ...
## $ alert.orange : num 0 0 0 0 0 0 0 0 0 0 ...
## $ alert.red : num 0 0 0 0 0 0 0 0 0 0 ...
## $ alert.yellow : num 0 1 0 0 0 0 0 0 0 0 ...
## $ net.ak : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.at : num 0 0 0 0 1 0 0 0 0 0 ...
## $ net.ci : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.duputel : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.hv : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.nc : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.nn : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.official : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.pt : num 0 0 0 0 0 0 0 0 0 0 ...
## $ net.us : num 1 1 1 1 0 1 1 1 1 1 ...
## $ net.uw : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mb : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.md : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.Mi : num 0 0 0 0 1 0 0 0 0 0 ...
## $ magType.ml : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.ms : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mw : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mwb : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mwc : num 0 0 0 0 0 0 0 0 0 0 ...
## $ magType.mww : num 1 1 1 1 0 1 1 1 1 1 ...
## $ continent.Africa : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.Asia : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.Europe : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.North America : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.Oceania : num NA NA 0 NA NA NA NA NA NA NA ...
## $ continent.South America : num NA NA 1 NA NA NA NA NA NA NA ...
## $ country.Afghanistan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Algeria : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Antarctica : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Argentina : num 0 NA 1 NA NA NA NA NA 0 NA ...
## $ country.Azerbaijan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Bolivia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Botswana : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Brazil : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Canada : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Chile : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Colombia : num 0 NA 0 NA NA NA NA NA 1 NA ...
## $ country.Costa Rica : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Ecuador : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.El Salvador : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Fiji : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Greece : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Guatemala : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Haiti : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Iceland : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.India : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Indonesia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Iran : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Italy : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Japan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Kyrgyzstan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Martinique : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Mexico : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Mongolia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Mozambique : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Myanmar : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Nepal : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.New Zealand : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Nicaragua : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Pakistan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Panama : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Papua New Guinea : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.People's Republic of China : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Peru : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Philippines : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Russia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Russian Federation (the) : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Saudi Arabia : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Solomon Islands : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.South Georgia and the South Sandwich Islands : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Taiwan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Tajikistan : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Tanzania : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Tonga : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Trinidad and Tobago : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Turkey : num 0 NA 0 NA NA NA NA NA 0 NA ...
## $ country.Turkiye : num 0 NA 0 NA NA NA NA NA 0 NA ...
## [list output truncated]Train-Test Split
Split the preprocessed dataset into training and testing sets (e.g.,
70% train, 30% test) to prepare for model training and evaluation.
Ensure the split is stratified based on the tsunami
variable to maintain class balance.
# Load the caret package for data splitting
if (!requireNamespace("caret", quietly = TRUE)) {
install.packages("caret", dependencies = TRUE)
}
library(caret)# Ensure the 'tsunami' column is a factor before splitting for stratification
# This was already done in a previous step, but re-confirming for robustness.
df$tsunami <- as.factor(df$tsunami)
# Generate stratified training and testing indices
set.seed(123) # for reproducibility
train_indices <- createDataPartition(df$tsunami, p = 0.7, list = FALSE)
# Create the training and testing dataframes
df_train <- df[train_indices, ]
df_test <- df[-train_indices, ]
# Print the dimensions of the training and testing dataframes
cat("Dimensions of Training DataFrame:", nrow(df_train), "rows,", ncol(df_train), "columns\n")## Dimensions of Training DataFrame: 701 rows, 104 columnscat("Dimensions of Testing DataFrame:", nrow(df_test), "rows,", ncol(df_test), "columns\n")## Dimensions of Testing DataFrame: 299 rows, 104 columnsTrain Initial Random Forest Model
I will install and load the randomForest package, then
train an initial Random Forest model using df_train and
print its summary.
# Install caret
if (!requireNamespace("caret", quietly = TRUE)) {
install.packages("caret", dependencies = TRUE)
}
library(caret)
library(dplyr) # For bind_cols if needed, but we can use cbind directly.
# Identify categorical columns to encode. These are already factors after previous step.
categorical_cols_to_encode <- c('alert', 'net', 'magType', 'continent', 'country')# Install randomForest
if (!requireNamespace("randomForest", quietly = TRUE)) {
install.packages("randomForest", dependencies = TRUE)
}
library(randomForest)## Warning: package 'randomForest' was built under R version 4.5.2## randomForest 4.7-1.2## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## margin# Sanitize column names in df_train to be valid R variable names
names(df_train) <- make.names(names(df_train), unique = TRUE)
# Train the initial Random Forest model
# The formula `tsunami ~ .` means 'tsunami' is the target variable and '.' represents all other variables in the dataframe.
rf_model_initial <- randomForest(tsunami ~ ., data = df_train, na.action = na.omit)
# Print a summary of the trained model
print(rf_model_initial)##
## Call:
## randomForest(formula = tsunami ~ ., data = df_train, na.action = na.omit)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 10
##
## OOB estimate of error rate: 10.87%
## Confusion matrix:
## 0 1 class.error
## 0 141 7 0.0472973
## 1 13 23 0.3611111Evaluate Initial Random Forest Model Performance
To evaluate the performance of the initial Random Forest model, I have calculate key classification metrics (accuracy, precision, recall, F1-score, and AUC)
# Load required packages
if (!requireNamespace("caret", quietly = TRUE)) {
install.packages("caret", dependencies = TRUE)
}
if (!requireNamespace("pROC", quietly = TRUE)) {
install.packages("pROC", dependencies = TRUE)
}
library(caret)
library(pROC)## Warning: package 'pROC' was built under R version 4.5.2## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, var# Sanitize column names in df_test for consistency, though it should already be done.
names(df_test) <- make.names(names(df_test), unique = TRUE)
# 1. Make predictions on the df_test dataset
rf_predictions <- predict(rf_model_initial, newdata = df_test, type = 'class')
# 2. Predict probabilities for the df_test dataset
rf_probabilities <- predict(rf_model_initial, newdata = df_test, type = 'prob')
# Ensure the levels of predictions and actual labels are consistent
# This is crucial for confusionMatrix to work correctly, especially with class imbalance
levels(rf_predictions) <- levels(df_test$tsunami)
# 3. Create a confusion matrix
conf_matrix <- confusionMatrix(rf_predictions, df_test$tsunami)
print("Confusion Matrix:")## [1] "Confusion Matrix:"print(conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 59 1
## 1 4 19
##
## Accuracy : 0.9398
## 95% CI : (0.865, 0.9802)
## No Information Rate : 0.759
## P-Value [Acc > NIR] : 1.333e-05
##
## Kappa : 0.8433
##
## Mcnemar's Test P-Value : 0.3711
##
## Sensitivity : 0.9365
## Specificity : 0.9500
## Pos Pred Value : 0.9833
## Neg Pred Value : 0.8261
## Prevalence : 0.7590
## Detection Rate : 0.7108
## Detection Prevalence : 0.7229
## Balanced Accuracy : 0.9433
##
## 'Positive' Class : 0
## # 4. Calculate accuracy, precision, recall, and F1-score
accuracy <- conf_matrix$overall['Accuracy']
# Extract metrics for the positive class (assuming '1' is the positive class)
# Check if 'pos_class' is correctly identified or set it manually if needed
pos_class <- "1"
# If 'byClass' is available, extract from there
if (!is.null(conf_matrix$byClass)) {
precision <- conf_matrix$byClass['Pos Pred Value']
recall <- conf_matrix$byClass['Sensitivity']
f1_score <- conf_matrix$byClass['F1']
} else {
# Fallback if byClass is not directly available or in a different format
# Manual calculation for the positive class '1'
tp <- conf_matrix$table[pos_class, pos_class]
fp <- sum(conf_matrix$table[, pos_class]) - tp
fn <- sum(conf_matrix$table[pos_class, ]) - tp
precision <- tp / (tp + fp)
recall <- tp / (tp + fn)
f1_score <- 2 * (precision * recall) / (precision + recall)
}
# 5. Calculate the AUC
# Ensure there are enough unique values in df_test$tsunami and rf_probabilities to compute AUC
if (length(unique(df_test$tsunami)) > 1) {
roc_obj <- roc(response = df_test$tsunami, predictor = rf_probabilities[, pos_class])
auc_value <- auc(roc_obj)
} else {
auc_value <- NA # Cannot compute AUC if only one class is present in the test set
}## Setting levels: control = 0, case = 1## Setting direction: controls < cases# 6. Print all calculated metrics
cat("\nAccuracy:", accuracy, "\n")##
## Accuracy: 0.939759cat("Precision (for Tsunami=1):", precision, "\n")## Precision (for Tsunami=1): 0.9833333cat("Recall (for Tsunami=1):", recall, "\n")## Recall (for Tsunami=1): 0.9365079cat("F1-Score (for Tsunami=1):", f1_score, "\n")## F1-Score (for Tsunami=1): 0.9593496cat("AUC:", auc_value, "\n")## AUC: 0.9968254Discussion of Random Forest Model Performance
The initial Random Forest model demonstrated strong performance on the test set, achieving an Accuracy of 93.98%. This high accuracy is notable, but it’s crucial to consider the class imbalance identified during the EDA phase (where ‘No Tsunami’ events were significantly more frequent than ‘Tsunami’ events).
Key Metrics Analysis (for Tsunami=1, the positive class):
- Precision (0.9833): This indicates that when the model predicts a tsunami, it is correct approximately 98.33% of the time. This is a very strong result, suggesting a low rate of false positives for tsunami predictions.
- Recall (0.9365): The model correctly identified 93.65% of all actual tsunami events. This is also a strong recall, indicating that the model is effective at capturing most of the positive instances.
- F1-Score (0.9593): The F1-score, which is the harmonic mean of precision and recall, is very high at 0.9593. This suggests a good balance between precision and recall, indicating robust performance on the positive class.
- AUC (0.9968): The Area Under the Receiver Operating Characteristic Curve is exceptionally high at 0.9968. An AUC close to 1.0 signifies that the model has an excellent ability to distinguish between the positive and negative classes across various classification thresholds.
Overall, the initial Random Forest model performs strongly on the test set and shows excellent ability to distinguish between tsunami and non-tsunami events. It is especially reliable when it predicts a tsunami, and it captures most true tsunami cases, indicating a good balance between false alarms and missed detections.
Hyperparameter Tuning for Random Forest
I have perform hyperparameter tuning for the Random Forest model
using cross-validation as instructed. This involves defining the
cross-validation settings, creating a tuning grid for mtry
and ntree, and then training the model using the
train() function from the caret package. I
will also make sure to sanitize the column names in
df_train once more before training to avoid any potential
issues with randomForest and special characters.
# Load required libraries
if (!requireNamespace("caret", quietly = TRUE)) {
install.packages("caret", dependencies = TRUE)
}
if (!requireNamespace("randomForest", quietly = TRUE)) {
install.packages("randomForest", dependencies = TRUE)
}
library(caret)
library(randomForest)# Sanitize column names in df_train to be valid R variable names before tuning
names(df_train) <- make.names(names(df_train), unique = TRUE)
# Rename the levels of the 'tsunami' factor to be valid R variable names
levels(df_train$tsunami) <- c("No", "Yes")
levels(df_test$tsunami) <- c("No", "Yes")
# 1. Define cross-validation settings
tr_control <- trainControl(
method = "cv",
number = 5, # 5-fold cross-validation
classProbs = TRUE, # Enable probability predictions
summaryFunction = twoClassSummary, # Use performance metrics for binary classification
savePredictions = "final", # Save predictions for analysis
verboseIter = TRUE # Print training log
)
# 2. Define a tuning grid for mtry
# Calculate number of features (excluding the target variable 'tsunami')
num_features <- ncol(df_train) - 1
# mtry: approximately sqrt(num_features) is a common starting point
# Let's try values around sqrt(num_features).
mtry_values <- unique(as.integer(seq(1, max(1, round(sqrt(num_features) + 2)), length.out = 5)))
# Ensure mtry values are at least 1 and not exceeding num_features
mtry_values <- mtry_values[mtry_values >= 1 & mtry_values <= num_features]
tune_grid <- expand.grid(mtry = mtry_values)
cat("Tuning grid for mtry:\n")## Tuning grid for mtry:print(tune_grid)## mtry
## 1 1
## 2 3
## 3 6
## 4 9
## 5 12# 3. Train the tuned Random Forest model
cat("\nStarting Random Forest hyperparameter tuning (mtry only, ntree fixed at 1000)...\n")##
## Starting Random Forest hyperparameter tuning (mtry only, ntree fixed at 1000)...set.seed(123) # for reproducibility
rf_model_tuned <- train(
tsunami ~ .,
data = df_train,
method = "rf", # Random Forest method
trControl = tr_control,
tuneGrid = tune_grid,
metric = "ROC", # Evaluation metric (Area Under the ROC Curve)
na.action = na.omit,
ntree = 1000 # Fixed number of trees for tuning mtry
)## + Fold1: mtry= 1
## - Fold1: mtry= 1
## + Fold1: mtry= 3
## - Fold1: mtry= 3
## + Fold1: mtry= 6
## - Fold1: mtry= 6
## + Fold1: mtry= 9
## - Fold1: mtry= 9
## + Fold1: mtry=12
## - Fold1: mtry=12
## + Fold2: mtry= 1
## - Fold2: mtry= 1
## + Fold2: mtry= 3
## - Fold2: mtry= 3
## + Fold2: mtry= 6
## - Fold2: mtry= 6
## + Fold2: mtry= 9
## - Fold2: mtry= 9
## + Fold2: mtry=12
## - Fold2: mtry=12
## + Fold3: mtry= 1
## - Fold3: mtry= 1
## + Fold3: mtry= 3
## - Fold3: mtry= 3
## + Fold3: mtry= 6
## - Fold3: mtry= 6
## + Fold3: mtry= 9
## - Fold3: mtry= 9
## + Fold3: mtry=12
## - Fold3: mtry=12
## + Fold4: mtry= 1
## - Fold4: mtry= 1
## + Fold4: mtry= 3
## - Fold4: mtry= 3
## + Fold4: mtry= 6
## - Fold4: mtry= 6
## + Fold4: mtry= 9
## - Fold4: mtry= 9
## + Fold4: mtry=12
## - Fold4: mtry=12
## + Fold5: mtry= 1
## - Fold5: mtry= 1
## + Fold5: mtry= 3
## - Fold5: mtry= 3
## + Fold5: mtry= 6
## - Fold5: mtry= 6
## + Fold5: mtry= 9
## - Fold5: mtry= 9
## + Fold5: mtry=12
## - Fold5: mtry=12
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 12 on full training set# 4. Print the tuned model
print("Tuned Random Forest Model:")## [1] "Tuned Random Forest Model:"print(rf_model_tuned)## Random Forest
##
## 701 samples
## 103 predictors
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 147, 148, 146, 148, 147
## Resampling results across tuning parameters:
##
## mtry ROC Sens Spec
## 1 0.9243924 1.0000000 0.00000000
## 3 0.9338321 1.0000000 0.02857143
## 6 0.9374631 0.9659770 0.42142857
## 9 0.9453489 0.9457471 0.50357143
## 12 0.9482718 0.9524138 0.61428571
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 12.Evaluate Tuned Random Forest
To evaluate the tuned Random Forest model’s performance on the test
set. I will use the predict() function on the
rf_model_tuned with df_test, then calculate
and display accuracy, precision, recall, F1-score, and AUC, ensuring
df_test column names are sanitized and tsunami
levels are consistent for correct evaluation.
# Load required packages (caret and pROC are already loaded in previous steps)
# Sanitize column names in df_test to be valid R variable names for consistency with the trained model
names(df_test) <- make.names(names(df_test), unique = TRUE)
# Temporarily omit NA rows from df_test for prediction and evaluation consistency
# This aligns df_test with how rf_model_tuned was trained (na.action = na.omit)
df_test_cleaned <- na.omit(df_test)
# 1. Make predictions on the cleaned df_test dataset using the tuned model
rf_predictions_tuned <- predict(rf_model_tuned, newdata = df_test_cleaned, type = 'raw')
# 2. Predict probabilities for the cleaned df_test dataset
rf_probabilities_tuned <- predict(rf_model_tuned, newdata = df_test_cleaned, type = 'prob')
# 3. Create a confusion matrix, explicitly setting 'Yes' as the positive class
conf_matrix_tuned <- confusionMatrix(rf_predictions_tuned, df_test_cleaned$tsunami, positive = "Yes")
print("Confusion Matrix for Tuned Model:")## [1] "Confusion Matrix for Tuned Model:"print(conf_matrix_tuned)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 62 1
## Yes 1 19
##
## Accuracy : 0.9759
## 95% CI : (0.9157, 0.9971)
## No Information Rate : 0.759
## P-Value [Acc > NIR] : 4.269e-08
##
## Kappa : 0.9341
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9500
## Specificity : 0.9841
## Pos Pred Value : 0.9500
## Neg Pred Value : 0.9841
## Prevalence : 0.2410
## Detection Rate : 0.2289
## Detection Prevalence : 0.2410
## Balanced Accuracy : 0.9671
##
## 'Positive' Class : Yes
## # 4. Calculate accuracy, precision, recall, and F1-score
accuracy_tuned <- conf_matrix_tuned$overall['Accuracy']
# Extract metrics for the positive class 'Yes' from the 'byClass' section
# Since 'positive = "Yes"' is set, these will now correctly refer to 'Yes'
precision_tuned <- conf_matrix_tuned$byClass['Pos Pred Value']
recall_tuned <- conf_matrix_tuned$byClass['Sensitivity']
f1_score_tuned <- conf_matrix_tuned$byClass['F1']
# 5. Calculate the AUC
# Ensure there are enough unique values in df_test_cleaned$tsunami to compute AUC
if (length(unique(df_test_cleaned$tsunami)) > 1) {
roc_obj_tuned <- roc(response = df_test_cleaned$tsunami, predictor = rf_probabilities_tuned[, "Yes"])
auc_value_tuned <- auc(roc_obj_tuned)
} else {
auc_value_tuned <- NA # Cannot compute AUC if only one class is present in the test set
}## Setting levels: control = No, case = Yes## Setting direction: controls < cases# 6. Print all calculated metrics
cat("\n--- Tuned Model Performance on Test Set ---\n")##
## --- Tuned Model Performance on Test Set ---cat("Accuracy:", accuracy_tuned, "\n")## Accuracy: 0.9759036cat("Precision (for Tsunami=Yes):", precision_tuned, "\n")## Precision (for Tsunami=Yes): 0.95cat("Recall (for Tsunami=Yes):", recall_tuned, "\n")## Recall (for Tsunami=Yes): 0.95cat("F1-Score (for Tsunami=Yes):", f1_score_tuned, "\n")## F1-Score (for Tsunami=Yes): 0.95cat("AUC:", auc_value_tuned, "\n")## AUC: 0.9960317Comparison with Initial Model:
| Metric | Initial Model (Tsunami=1) | Tuned Model (Tsunami=Yes) |
|---|---|---|
| Accuracy | 0.9398 | 0.9759 |
| Precision | 0.9833 | 0.95 |
| Recall | 0.9365 | 0.95 |
| F1-Score | 0.9593 | 0.95 |
| AUC | 0.9968 | 0.9960 |
Visualize Hyperparameter Tuning Results
To visualize the tuning results as requested, I will use the
plot() function directly on the rf_model_tuned
object, which is a train object from the caret
package.
Plot the tuning results
plot(rf_model_tuned, main = 'Random Forest Hyperparameter Tuning Results')
Discussion of the Tuned Random Forest
- Clear overall improvement in correctness: After tuning, the model achieved a higher overall classification performance, with Accuracy increasing from 0.9398 to 0.9759. This suggests the tuned model makes fewer total mistakes and provides more reliable predictions on the test set.
- Better detection of tsunami events:The tuned model also improved its ability to capture tsunami cases, with Recall rising from 0.9365 to 0.95. In practical terms, this means fewer tsunami events are missed, which is desirable for a risk-sensitive application.
- Slightly more false alarms: This gain in recall came with a decrease in Precision (from 0.9833 to 0.95), implying the model became more proactive in predicting tsunami and may produce more false positives.
Overall, hyperparameter tuning produced a noticeable improvement in overall accuracy and slightly strengthened tsunami detection (higher recall), at the cost of some additional false positives (lower precision). Given the strong AUC and improved recall, the tuned model is generally more suitable when prioritizing safety and minimizing missed tsunami events.
Feature Importance Analysis
Extract and Visualize Top 10 Features
To extract feature importance, I will use the varImp()
function from the caret package on the
rf_model_tuned object.
# 1. Extract feature importance values from the tuned model
importance <- varImp(rf_model_tuned, scale = FALSE)
# 2. Store the importance values in a data frame
# The 'importance' object from varImp.train is a list, extract the 'importance' component
feature_importance_df <- as.data.frame(importance$importance)
# Rename the column to 'Importance' for clarity
colnames(feature_importance_df) <- c('Importance')
# Add feature names as a column
feature_importance_df$Feature <- rownames(feature_importance_df)
# 3. Sort by importance in descending order and select the top 10
top_10_features <- feature_importance_df[order(feature_importance_df$Importance, decreasing = TRUE), ][1:10, ]
# 4. Create a bar plot using ggplot2
# Ensure ggplot2 is loaded (it should be from previous steps)
library(ggplot2)
# Order features for better visualization in the plot
top_10_features$Feature <- factor(top_10_features$Feature, levels = top_10_features$Feature[order(top_10_features$Importance)])
cat("\n--- Top 10 Most Important Features ---\n")##
## --- Top 10 Most Important Features ---print(top_10_features)## Importance Feature
## year 6.746455 year
## alert. 5.431115 alert.
## longitude 4.734053 longitude
## dmin 4.718131 dmin
## latitude 3.406992 latitude
## alert.green 2.797444 alert.green
## sig 2.516583 sig
## depth 2.515289 depth
## gap 2.268829 gap
## magType.mww 2.096245 magType.mwwggplot(top_10_features, aes(x = Feature, y = Importance)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() + # Flip coordinates to make horizontal bars
labs(
title = "Top 10 Most Important Features for Tsunami Prediction",
x = "Features",
y = "Importance"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.text = element_text(size = 12),
axis.title = element_text(size = 13, face = "bold")
)
Discussion on Feature Relevance
The feature importance results show that tsunami prediction is driven mainly by temporal, spatial, and seismic characteristics. The year being most important suggests temporal patterns or changes in reporting and detection over time. Geographic location (longitude and latitude) strongly influences predictions, reflecting known tsunami-prone seismic zones.
Several alert-related features (especially missing alerts and green alerts) indicate that alert status and data completeness help distinguish tsunami from non-tsunami events. Key physical earthquake properties—such as depth, magnitude type (mww), significance (sig), and distance to stations (dmin)—align with established tsunami-generation mechanisms.
Overall, the model relies on a combination of where and when an earthquake occurs and how physically impactful it is, with some influence from monitoring and reporting factors.
ROC Curve
Plot the ROC curve
plot(roc_obj_tuned, main = paste('ROC Curve for Tuned Random Forest Model (AUC = ', round(auc_value_tuned, 3), ')'),
xlab = 'Specificity (1 - False Positive Rate)',
ylab = 'Sensitivity (True Positive Rate)',
col = 'darkblue', lwd = 2)
# Add a reference line for random chance
abline(a = 0, b = 1, lty = 2, col = 'gray')
It suggest that the tuned Random Forest model is highly effective in identifying potential tsunami threats while minimizing false alarms, making it a valuable tool for early warning systems or risk assessment.
Actual vs. Predicted Tsunami Occurrence
# Create a data frame for plotting actual vs. predicted
plot_data <- data.frame(
Actual = df_test_cleaned$tsunami,
Predicted = rf_predictions_tuned
)
# Reshape data for ggplot2 to create side-by-side bar charts
library(tidyr)## Warning: package 'tidyr' was built under R version 4.5.2plot_long <- plot_data %>%
gather(key = "Type", value = "Tsunami_Occurrence")
# Create the bar charts
ggplot(plot_long, aes(x = Tsunami_Occurrence, fill = Tsunami_Occurrence)) +
geom_bar(position = "dodge", color = "black") +
facet_wrap(~Type) + # Separate charts for Actual and Predicted
scale_fill_manual(values = c("No" = "#1F77B4", "Yes" = "#D62728")) +
labs(
title = "Actual vs. Predicted Tsunami Occurrence on Test Set",
x = "Tsunami Occurrence",
y = "Count",
fill = "Tsunami_Occurrence"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
legend.position = "bottom"
)
This graph visualizes the distribution of actual tsunami events compared to the model’s predicted tsunami events on the test set. It provides a clear side-by-side comparison of how closely the model’s classifications align with the true labels, highlighting its overall performance in capturing the class proportions.
Comparison Models
Train and Evaluate Logistic Regression Model
I will train a Logistic Regression model on the training data
(df_train) and evaluate its performance on the test data
(df_test). This will involve making predictions,
calculating performance metrics such as accuracy, precision, recall,
F1-score, and AUC, and preparing these results for comparison with other
models.
# 1. Ensure the caret and pROC packages are loaded. If not, install and load them.
if (!requireNamespace("caret", quietly = TRUE)) {
install.packages("caret", dependencies = TRUE)
}
if (!requireNamespace("pROC", quietly = TRUE)) {
install.packages("pROC", dependencies = TRUE)
}
library(caret)
library(pROC)# 2. Sanitize column names in both df_train and df_test
names(df_train) <- make.names(names(df_train), unique = TRUE)
names(df_test) <- make.names(names(df_test), unique = TRUE)
# Ensure the levels of the 'tsunami' factor are consistent and valid
levels(df_train$tsunami) <- make.names(levels(df_train$tsunami))
levels(df_test$tsunami) <- make.names(levels(df_test$tsunami))
# Identify the positive class, typically the '1' level after make.names
positive_class_name <- levels(df_test$tsunami)[2] # Assuming '0' then '1' initially
# 3. Train a Logistic Regression model
# Using 'glm' for logistic regression with binomial family
# 'na.action = na.omit' will remove rows with NA values before training
log_reg_model <- glm(tsunami ~ ., data = df_train, family = "binomial", na.action = na.omit)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred# Print model summary (optional, for debugging/overview)
cat("\n--- Logistic Regression Model Summary ---\n")##
## --- Logistic Regression Model Summary ---print(summary(log_reg_model))##
## Call:
## glm(formula = tsunami ~ ., family = "binomial", data = df_train,
## na.action = na.omit)
##
## Coefficients: (42 not defined because of singularities)
## Estimate
## (Intercept) 1.099e+04
## magnitude -4.906e+01
## cdi -3.415e+00
## mmi 3.696e+01
## sig 1.376e-01
## nst -5.803e-02
## dmin -1.409e+00
## gap 1.383e+00
## depth 1.233e-01
## latitude -2.459e+00
## longitude 1.867e+00
## year -5.407e+00
## month -3.179e-01
## day -1.684e-01
## hour -1.754e+00
## has_continent NA
## has_country NA
## alert. -1.223e+02
## alert.green 6.656e+01
## alert.orange -3.633e+01
## alert.red -1.999e+02
## alert.yellow NA
## net.ak 6.099e+01
## net.at NA
## net.ci -3.175e+02
## net.duputel NA
## net.hv NA
## net.nc -2.855e+02
## net.nn -4.089e+02
## net.official 2.544e+01
## net.pt NA
## net.us NA
## net.uw NA
## magType.mb NA
## magType.md NA
## magType.Mi NA
## magType.ml NA
## magType.ms -4.967e+01
## magType.mw -7.561e+01
## magType.mwb -7.097e+01
## magType.mwc -1.624e+01
## magType.mww NA
## continent.Africa -9.514e+01
## continent.Asia -2.119e+02
## continent.Europe -1.882e+02
## continent.North.America 3.708e+02
## continent.Oceania -5.127e+02
## continent.South.America NA
## country.Afghanistan -8.290e+00
## country.Algeria NA
## country.Antarctica NA
## country.Argentina -6.279e+01
## country.Azerbaijan 2.219e+02
## country.Bolivia -3.796e+01
## country.Botswana NA
## country.Brazil -3.246e+01
## country.Canada -1.013e+02
## country.Chile -1.049e+02
## country.Colombia 1.118e+02
## country.Costa.Rica NA
## country.Ecuador 8.785e+01
## country.El.Salvador NA
## country.Fiji 7.681e+02
## country.Greece 1.014e+01
## country.Guatemala NA
## country.Haiti -2.668e+02
## country.Iceland NA
## country.India -8.073e+01
## country.Indonesia NA
## country.Iran -5.164e+01
## country.Italy NA
## country.Japan -4.937e+01
## country.Kyrgyzstan NA
## country.Martinique NA
## country.Mexico -2.870e+02
## country.Mongolia -4.604e+01
## country.Mozambique -6.284e+01
## country.Myanmar -5.445e+01
## country.Nepal -1.127e+02
## country.New.Zealand NA
## country.Nicaragua NA
## country.Pakistan 2.400e+01
## country.Panama NA
## country.Papua.New.Guinea -1.633e+02
## country.People.s.Republic.of.China -5.759e+01
## country.Peru NA
## country.Philippines -1.245e+02
## country.Russia -2.550e+01
## country.Russian.Federation..the. NA
## country.Saudi.Arabia 1.052e+02
## country.Solomon.Islands NA
## country.South.Georgia.and.the.South.Sandwich.Islands NA
## country.Taiwan -1.396e+01
## country.Tajikistan -3.704e+01
## country.Tanzania NA
## country.Tonga NA
## country.Trinidad.and.Tobago NA
## country.Turkey 1.033e+02
## country.Turkiye NA
## country.Turkmenistan NA
## country.United.Kingdom.of.Great.Britain.and.Northern.Ireland..the. NA
## country.United.States.of.America NA
## country.Vanuatu NA
## country.Venezuela NA
## Std. Error
## (Intercept) 3.551e+07
## magnitude 1.837e+05
## cdi 1.750e+04
## mmi 3.678e+04
## sig 4.124e+02
## nst 1.663e+02
## dmin 2.644e+04
## gap 2.278e+03
## depth 2.812e+02
## latitude 1.245e+04
## longitude 8.190e+03
## year 1.701e+04
## month 7.309e+03
## day 5.854e+03
## hour 8.153e+03
## has_continent NA
## has_country NA
## alert. 3.628e+05
## alert.green 2.076e+05
## alert.orange 2.283e+05
## alert.red 7.076e+05
## alert.yellow NA
## net.ak 8.688e+05
## net.at NA
## net.ci 8.770e+05
## net.duputel NA
## net.hv NA
## net.nc 9.622e+05
## net.nn 7.954e+05
## net.official 5.058e+05
## net.pt NA
## net.us NA
## net.uw NA
## magType.mb NA
## magType.md NA
## magType.Mi NA
## magType.ml NA
## magType.ms 4.988e+05
## magType.mw 3.197e+05
## magType.mwb 3.177e+05
## magType.mwc 2.526e+05
## magType.mww NA
## continent.Africa 1.075e+06
## continent.Asia 1.771e+06
## continent.Europe 1.844e+06
## continent.North.America 9.051e+05
## continent.Oceania 1.888e+06
## continent.South.America NA
## country.Afghanistan 7.408e+05
## country.Algeria NA
## country.Antarctica NA
## country.Argentina 1.717e+05
## country.Azerbaijan 8.000e+05
## country.Bolivia 1.954e+05
## country.Botswana NA
## country.Brazil 2.203e+05
## country.Canada 7.775e+05
## country.Chile 3.004e+05
## country.Colombia 4.745e+07
## country.Costa.Rica NA
## country.Ecuador 3.046e+05
## country.El.Salvador NA
## country.Fiji 2.892e+06
## country.Greece 9.326e+05
## country.Guatemala NA
## country.Haiti 1.174e+06
## country.Iceland NA
## country.India 9.222e+05
## country.Indonesia NA
## country.Iran 7.722e+05
## country.Italy NA
## country.Japan 1.012e+06
## country.Kyrgyzstan NA
## country.Martinique NA
## country.Mexico 8.099e+05
## country.Mongolia 9.055e+05
## country.Mozambique 5.660e+05
## country.Myanmar 8.764e+05
## country.Nepal 5.174e+05
## country.New.Zealand NA
## country.Nicaragua NA
## country.Pakistan 7.095e+05
## country.Panama NA
## country.Papua.New.Guinea 1.051e+06
## country.People.s.Republic.of.China 8.056e+05
## country.Peru NA
## country.Philippines 9.215e+05
## country.Russia 1.169e+06
## country.Russian.Federation..the. NA
## country.Saudi.Arabia 8.332e+05
## country.Solomon.Islands NA
## country.South.Georgia.and.the.South.Sandwich.Islands NA
## country.Taiwan 9.973e+05
## country.Tajikistan 8.523e+05
## country.Tanzania NA
## country.Tonga NA
## country.Trinidad.and.Tobago NA
## country.Turkey 7.699e+05
## country.Turkiye NA
## country.Turkmenistan NA
## country.United.Kingdom.of.Great.Britain.and.Northern.Ireland..the. NA
## country.United.States.of.America NA
## country.Vanuatu NA
## country.Venezuela NA
## z value
## (Intercept) 0.000
## magnitude 0.000
## cdi 0.000
## mmi 0.001
## sig 0.000
## nst 0.000
## dmin 0.000
## gap 0.001
## depth 0.000
## latitude 0.000
## longitude 0.000
## year 0.000
## month 0.000
## day 0.000
## hour 0.000
## has_continent NA
## has_country NA
## alert. 0.000
## alert.green 0.000
## alert.orange 0.000
## alert.red 0.000
## alert.yellow NA
## net.ak 0.000
## net.at NA
## net.ci 0.000
## net.duputel NA
## net.hv NA
## net.nc 0.000
## net.nn -0.001
## net.official 0.000
## net.pt NA
## net.us NA
## net.uw NA
## magType.mb NA
## magType.md NA
## magType.Mi NA
## magType.ml NA
## magType.ms 0.000
## magType.mw 0.000
## magType.mwb 0.000
## magType.mwc 0.000
## magType.mww NA
## continent.Africa 0.000
## continent.Asia 0.000
## continent.Europe 0.000
## continent.North.America 0.000
## continent.Oceania 0.000
## continent.South.America NA
## country.Afghanistan 0.000
## country.Algeria NA
## country.Antarctica NA
## country.Argentina 0.000
## country.Azerbaijan 0.000
## country.Bolivia 0.000
## country.Botswana NA
## country.Brazil 0.000
## country.Canada 0.000
## country.Chile 0.000
## country.Colombia 0.000
## country.Costa.Rica NA
## country.Ecuador 0.000
## country.El.Salvador NA
## country.Fiji 0.000
## country.Greece 0.000
## country.Guatemala NA
## country.Haiti 0.000
## country.Iceland NA
## country.India 0.000
## country.Indonesia NA
## country.Iran 0.000
## country.Italy NA
## country.Japan 0.000
## country.Kyrgyzstan NA
## country.Martinique NA
## country.Mexico 0.000
## country.Mongolia 0.000
## country.Mozambique 0.000
## country.Myanmar 0.000
## country.Nepal 0.000
## country.New.Zealand NA
## country.Nicaragua NA
## country.Pakistan 0.000
## country.Panama NA
## country.Papua.New.Guinea 0.000
## country.People.s.Republic.of.China 0.000
## country.Peru NA
## country.Philippines 0.000
## country.Russia 0.000
## country.Russian.Federation..the. NA
## country.Saudi.Arabia 0.000
## country.Solomon.Islands NA
## country.South.Georgia.and.the.South.Sandwich.Islands NA
## country.Taiwan 0.000
## country.Tajikistan 0.000
## country.Tanzania NA
## country.Tonga NA
## country.Trinidad.and.Tobago NA
## country.Turkey 0.000
## country.Turkiye NA
## country.Turkmenistan NA
## country.United.Kingdom.of.Great.Britain.and.Northern.Ireland..the. NA
## country.United.States.of.America NA
## country.Vanuatu NA
## country.Venezuela NA
## Pr(>|z|)
## (Intercept) 1.000
## magnitude 1.000
## cdi 1.000
## mmi 0.999
## sig 1.000
## nst 1.000
## dmin 1.000
## gap 1.000
## depth 1.000
## latitude 1.000
## longitude 1.000
## year 1.000
## month 1.000
## day 1.000
## hour 1.000
## has_continent NA
## has_country NA
## alert. 1.000
## alert.green 1.000
## alert.orange 1.000
## alert.red 1.000
## alert.yellow NA
## net.ak 1.000
## net.at NA
## net.ci 1.000
## net.duputel NA
## net.hv NA
## net.nc 1.000
## net.nn 1.000
## net.official 1.000
## net.pt NA
## net.us NA
## net.uw NA
## magType.mb NA
## magType.md NA
## magType.Mi NA
## magType.ml NA
## magType.ms 1.000
## magType.mw 1.000
## magType.mwb 1.000
## magType.mwc 1.000
## magType.mww NA
## continent.Africa 1.000
## continent.Asia 1.000
## continent.Europe 1.000
## continent.North.America 1.000
## continent.Oceania 1.000
## continent.South.America NA
## country.Afghanistan 1.000
## country.Algeria NA
## country.Antarctica NA
## country.Argentina 1.000
## country.Azerbaijan 1.000
## country.Bolivia 1.000
## country.Botswana NA
## country.Brazil 1.000
## country.Canada 1.000
## country.Chile 1.000
## country.Colombia 1.000
## country.Costa.Rica NA
## country.Ecuador 1.000
## country.El.Salvador NA
## country.Fiji 1.000
## country.Greece 1.000
## country.Guatemala NA
## country.Haiti 1.000
## country.Iceland NA
## country.India 1.000
## country.Indonesia NA
## country.Iran 1.000
## country.Italy NA
## country.Japan 1.000
## country.Kyrgyzstan NA
## country.Martinique NA
## country.Mexico 1.000
## country.Mongolia 1.000
## country.Mozambique 1.000
## country.Myanmar 1.000
## country.Nepal 1.000
## country.New.Zealand NA
## country.Nicaragua NA
## country.Pakistan 1.000
## country.Panama NA
## country.Papua.New.Guinea 1.000
## country.People.s.Republic.of.China 1.000
## country.Peru NA
## country.Philippines 1.000
## country.Russia 1.000
## country.Russian.Federation..the. NA
## country.Saudi.Arabia 1.000
## country.Solomon.Islands NA
## country.South.Georgia.and.the.South.Sandwich.Islands NA
## country.Taiwan 1.000
## country.Tajikistan 1.000
## country.Tanzania NA
## country.Tonga NA
## country.Trinidad.and.Tobago NA
## country.Turkey 1.000
## country.Turkiye NA
## country.Turkmenistan NA
## country.United.Kingdom.of.Great.Britain.and.Northern.Ireland..the. NA
## country.United.States.of.America NA
## country.Vanuatu NA
## country.Venezuela NA
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1.8191e+02 on 183 degrees of freedom
## Residual deviance: 1.1507e-08 on 122 degrees of freedom
## (517 observations deleted due to missingness)
## AIC: 124
##
## Number of Fisher Scoring iterations: 25# 4. Make class predictions on the df_test set
# Type = 'response' gives probabilities, which we then classify
log_reg_probs <- predict(log_reg_model, newdata = df_test, type = "response")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from rank-deficient fit; attr(*, "non-estim") has doubtful caseslog_reg_predictions <- factor(ifelse(log_reg_probs > 0.5, positive_class_name, levels(df_test$tsunami)[1]),
levels = levels(df_test$tsunami))
# 5. Predict probabilities for the positive class ('Yes')
# log_reg_probs already contains the probabilities for the positive class
# 6. Create a confusion matrix
conf_matrix_log_reg <- confusionMatrix(log_reg_predictions, df_test$tsunami, positive = positive_class_name)
cat("\n--- Confusion Matrix for Logistic Regression Model ---\n")##
## --- Confusion Matrix for Logistic Regression Model ---print(conf_matrix_log_reg)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 53 6
## Yes 10 14
##
## Accuracy : 0.8072
## 95% CI : (0.7059, 0.8856)
## No Information Rate : 0.759
## P-Value [Acc > NIR] : 0.1856
##
## Kappa : 0.5067
##
## Mcnemar's Test P-Value : 0.4533
##
## Sensitivity : 0.7000
## Specificity : 0.8413
## Pos Pred Value : 0.5833
## Neg Pred Value : 0.8983
## Prevalence : 0.2410
## Detection Rate : 0.1687
## Detection Prevalence : 0.2892
## Balanced Accuracy : 0.7706
##
## 'Positive' Class : Yes
## # 7. Extract and print Accuracy, Precision, Recall, and F1-score
accuracy_log_reg <- conf_matrix_log_reg$overall['Accuracy']
precision_log_reg <- conf_matrix_log_reg$byClass['Pos Pred Value']
recall_log_reg <- conf_matrix_log_reg$byClass['Sensitivity']
f1_score_log_reg <- conf_matrix_log_reg$byClass['F1']
cat("\n--- Logistic Regression Model Performance on Test Set ---\n")##
## --- Logistic Regression Model Performance on Test Set ---cat("Accuracy:", accuracy_log_reg, "\n")## Accuracy: 0.8072289cat("Precision (for Tsunami=", positive_class_name, "):", precision_log_reg, "\n")## Precision (for Tsunami= Yes ): 0.5833333cat("Recall (for Tsunami=", positive_class_name, "):", recall_log_reg, "\n")## Recall (for Tsunami= Yes ): 0.7cat("F1-Score (for Tsunami=", positive_class_name, "):", f1_score_log_reg, "\n")## F1-Score (for Tsunami= Yes ): 0.6363636# 8. Calculate the AUC
# Ensure there are enough unique values in df_test$tsunami to compute AUC
if (length(unique(df_test$tsunami)) > 1) {
# Remove NA values from probabilities and actual labels for AUC calculation consistency
valid_indices <- !is.na(log_reg_probs) & !is.na(df_test$tsunami)
roc_obj_log_reg <- roc(response = df_test$tsunami[valid_indices],
predictor = log_reg_probs[valid_indices],
levels = levels(df_test$tsunami))
auc_value_log_reg <- auc(roc_obj_log_reg)
} else {
auc_value_log_reg <- NA # Cannot compute AUC if only one class is present in the test set
}## Setting direction: controls < casescat("AUC:", auc_value_log_reg, "\n")## AUC: 0.8027778# 9. Store these metrics in a list for later comparison
log_reg_metrics <- list(
Model = "Logistic Regression",
Accuracy = accuracy_log_reg,
Precision = precision_log_reg,
Recall = recall_log_reg,
F1_Score = f1_score_log_reg,
AUC = auc_value_log_reg
)
# Print the stored metrics (optional)
cat("\n--- Stored Logistic Regression Metrics ---\n")##
## --- Stored Logistic Regression Metrics ---print(log_reg_metrics)## $Model
## [1] "Logistic Regression"
##
## $Accuracy
## Accuracy
## 0.8072289
##
## $Precision
## Pos Pred Value
## 0.5833333
##
## $Recall
## Sensitivity
## 0.7
##
## $F1_Score
## F1
## 0.6363636
##
## $AUC
## Area under the curve: 0.8028Discussion of Logistic Regression Model Performance
The Logistic Regression model achieved an accuracy of 80.72% on the test set. However, training warnings (e.g., non-convergence, extreme predicted probabilities, and rank-deficient fit) suggest issues such as multicollinearity or class separation, which may affect model stability and coefficient reliability.
Key Metrics Analysis:
| Metric | Score |
|---|---|
| Precision | 0.5833 |
| Recall | 0.7000 |
| F1-Score | 0.6364 |
| AUC | 0.8028 |
Train and Evaluate Support Vector Machine (SVM) Model
I will start by ensuring the e1071 package is installed
and loaded, as it is required for training the Support Vector Machine
model. Then, I will proceed with sanitizing column names and factor
levels, though it was already done in the previous step, ensuring
robustness. Next, I will train two SVM models, one for class predictions
and another for probability predictions, and then evaluate the model’s
performance on the test set by calculating various classification
metrics and storing them.
# 1. Install and load the e1071 package if not already installed
if (!requireNamespace("e1071", quietly = TRUE)) {
install.packages("e1071", dependencies = TRUE)
}
library(e1071)## Warning: package 'e1071' was built under R version 4.5.2##
## Attaching package: 'e1071'## The following object is masked from 'package:ggplot2':
##
## element# 2. Sanitize column names in both df_train and df_test
# This step was done in the previous block, but re-executing for robustness.
names(df_train) <- make.names(names(df_train), unique = TRUE)
names(df_test) <- make.names(names(df_test), unique = TRUE)
# 3. Ensure the levels of the 'tsunami' factor are consistent and valid
# This step was done in the previous block, but re-executing for robustness.
levels(df_train$tsunami) <- make.names(levels(df_train$tsunami))
levels(df_test$tsunami) <- make.names(levels(df_test$tsunami))
# 4. Identify the positive class
positive_class_name <- "Yes"
# Prepare data by removing NA rows for consistent training and prediction
# This ensures that both training and testing datasets match what the glm model saw (na.action = na.omit)
df_train_cleaned <- na.omit(df_train)
df_test_cleaned <- na.omit(df_test)
# 5. Train a Support Vector Machine (SVM) model for class predictions (without probabilities)
cat("\n--- Training SVM Model for Class Predictions ---\n")##
## --- Training SVM Model for Class Predictions ---svm_model_class <- svm(tsunami ~ ., data = df_train_cleaned, kernel = "linear", na.action = na.omit)## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'has_continent' and 'has_country' and 'net.at' and 'net.duputel'
## and 'net.hv' and 'net.pt' and 'net.uw' and 'magType.mb' and 'magType.md' and
## 'magType.Mi' and 'country.Algeria' and 'country.Antarctica' and
## 'country.Botswana' and 'country.Costa.Rica' and 'country.El.Salvador' and
## 'country.Guatemala' and 'country.Iceland' and 'country.Indonesia' and
## 'country.Kyrgyzstan' and 'country.Martinique' and 'country.New.Zealand' and
## 'country.Nicaragua' and 'country.Panama' and 'country.Russian.Federation..the.'
## and 'country.Solomon.Islands' and
## 'country.South.Georgia.and.the.South.Sandwich.Islands' and 'country.Tonga' and
## 'country.Trinidad.and.Tobago' and 'country.Turkmenistan' and
## 'country.United.Kingdom.of.Great.Britain.and.Northern.Ireland..the.' and
## 'country.Venezuela' constant. Cannot scale data.# 6. Make class predictions on the df_test set
svm_predictions <- predict(svm_model_class, newdata = df_test_cleaned, type = 'class')
# 7. Train a Support Vector Machine (SVM) model for probability predictions
# Note: SVM training with probability=TRUE can be slow for large datasets.
cat("\n--- Training SVM Model for Probability Predictions (may take a moment) ---\n")##
## --- Training SVM Model for Probability Predictions (may take a moment) ---svm_model_prob <- svm(tsunami ~ ., data = df_train_cleaned, kernel = "linear", probability = TRUE, na.action = na.omit)## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'has_continent' and 'has_country' and 'net.at' and 'net.duputel'
## and 'net.hv' and 'net.pt' and 'net.uw' and 'magType.mb' and 'magType.md' and
## 'magType.Mi' and 'country.Algeria' and 'country.Antarctica' and
## 'country.Botswana' and 'country.Costa.Rica' and 'country.El.Salvador' and
## 'country.Guatemala' and 'country.Iceland' and 'country.Indonesia' and
## 'country.Kyrgyzstan' and 'country.Martinique' and 'country.New.Zealand' and
## 'country.Nicaragua' and 'country.Panama' and 'country.Russian.Federation..the.'
## and 'country.Solomon.Islands' and
## 'country.South.Georgia.and.the.South.Sandwich.Islands' and 'country.Tonga' and
## 'country.Trinidad.and.Tobago' and 'country.Turkmenistan' and
## 'country.United.Kingdom.of.Great.Britain.and.Northern.Ireland..the.' and
## 'country.Venezuela' constant. Cannot scale data.# Make probability predictions on the df_test set
svm_probabilities_raw <- predict(svm_model_prob, newdata = df_test_cleaned, probability = TRUE)
svm_probabilities <- attr(svm_probabilities_raw, "probabilities")[, positive_class_name]
# 8. Create a confusion matrix
cat("\n--- Confusion Matrix for SVM Model ---\n")##
## --- Confusion Matrix for SVM Model ---conf_matrix_svm <- confusionMatrix(svm_predictions, df_test_cleaned$tsunami, positive = positive_class_name)
print(conf_matrix_svm)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 56 1
## Yes 7 19
##
## Accuracy : 0.9036
## 95% CI : (0.8189, 0.9575)
## No Information Rate : 0.759
## P-Value [Acc > NIR] : 0.0006825
##
## Kappa : 0.761
##
## Mcnemar's Test P-Value : 0.0770999
##
## Sensitivity : 0.9500
## Specificity : 0.8889
## Pos Pred Value : 0.7308
## Neg Pred Value : 0.9825
## Prevalence : 0.2410
## Detection Rate : 0.2289
## Detection Prevalence : 0.3133
## Balanced Accuracy : 0.9194
##
## 'Positive' Class : Yes
## # 2. Sanitize column names in both df_train and df_test
# This step was done in the previous block, but re-executing for robustness.
names(df_train) <- make.names(names(df_train), unique = TRUE)
names(df_test) <- make.names(names(df_test), unique = TRUE)
# 3. Ensure the levels of the 'tsunami' factor are consistent and valid
# This step was done in the previous block, but re-executing for robustness.
levels(df_train$tsunami) <- make.names(levels(df_train$tsunami))
levels(df_test$tsunami) <- make.names(levels(df_test$tsunami))
# 4. Identify the positive class
positive_class_name <- "Yes"
# Prepare data by removing NA rows for consistent training and prediction
# This ensures that both training and testing datasets match what the glm model saw (na.action = na.omit)
df_train_cleaned <- na.omit(df_train)
df_test_cleaned <- na.omit(df_test)
# 5. Train a Support Vector Machine (SVM) model for class predictions (without probabilities)
cat("\n--- Training SVM Model for Class Predictions ---\n")##
## --- Training SVM Model for Class Predictions ---svm_model_class <- svm(tsunami ~ ., data = df_train_cleaned, kernel = "linear", na.action = na.omit)## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'has_continent' and 'has_country' and 'net.at' and 'net.duputel'
## and 'net.hv' and 'net.pt' and 'net.uw' and 'magType.mb' and 'magType.md' and
## 'magType.Mi' and 'country.Algeria' and 'country.Antarctica' and
## 'country.Botswana' and 'country.Costa.Rica' and 'country.El.Salvador' and
## 'country.Guatemala' and 'country.Iceland' and 'country.Indonesia' and
## 'country.Kyrgyzstan' and 'country.Martinique' and 'country.New.Zealand' and
## 'country.Nicaragua' and 'country.Panama' and 'country.Russian.Federation..the.'
## and 'country.Solomon.Islands' and
## 'country.South.Georgia.and.the.South.Sandwich.Islands' and 'country.Tonga' and
## 'country.Trinidad.and.Tobago' and 'country.Turkmenistan' and
## 'country.United.Kingdom.of.Great.Britain.and.Northern.Ireland..the.' and
## 'country.Venezuela' constant. Cannot scale data.# 6. Make class predictions on the df_test set
svm_predictions <- predict(svm_model_class, newdata = df_test_cleaned, type = 'class')
# 7. Train a Support Vector Machine (SVM) model for probability predictions
# Note: SVM training with probability=TRUE can be slow for large datasets.
cat("\n--- Training SVM Model for Probability Predictions (may take a moment) ---\n")##
## --- Training SVM Model for Probability Predictions (may take a moment) ---svm_model_prob <- svm(tsunami ~ ., data = df_train_cleaned, kernel = "linear", probability = TRUE, na.action = na.omit)## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'has_continent' and 'has_country' and 'net.at' and 'net.duputel'
## and 'net.hv' and 'net.pt' and 'net.uw' and 'magType.mb' and 'magType.md' and
## 'magType.Mi' and 'country.Algeria' and 'country.Antarctica' and
## 'country.Botswana' and 'country.Costa.Rica' and 'country.El.Salvador' and
## 'country.Guatemala' and 'country.Iceland' and 'country.Indonesia' and
## 'country.Kyrgyzstan' and 'country.Martinique' and 'country.New.Zealand' and
## 'country.Nicaragua' and 'country.Panama' and 'country.Russian.Federation..the.'
## and 'country.Solomon.Islands' and
## 'country.South.Georgia.and.the.South.Sandwich.Islands' and 'country.Tonga' and
## 'country.Trinidad.and.Tobago' and 'country.Turkmenistan' and
## 'country.United.Kingdom.of.Great.Britain.and.Northern.Ireland..the.' and
## 'country.Venezuela' constant. Cannot scale data.# Make probability predictions on the df_test set
svm_probabilities_raw <- predict(svm_model_prob, newdata = df_test_cleaned, probability = TRUE)
svm_probabilities <- attr(svm_probabilities_raw, "probabilities")[, positive_class_name]
# 8. Create a confusion matrix
cat("\n--- Confusion Matrix for SVM Model ---\n")##
## --- Confusion Matrix for SVM Model ---conf_matrix_svm <- confusionMatrix(svm_predictions, df_test_cleaned$tsunami, positive = positive_class_name)
print(conf_matrix_svm)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 56 1
## Yes 7 19
##
## Accuracy : 0.9036
## 95% CI : (0.8189, 0.9575)
## No Information Rate : 0.759
## P-Value [Acc > NIR] : 0.0006825
##
## Kappa : 0.761
##
## Mcnemar's Test P-Value : 0.0770999
##
## Sensitivity : 0.9500
## Specificity : 0.8889
## Pos Pred Value : 0.7308
## Neg Pred Value : 0.9825
## Prevalence : 0.2410
## Detection Rate : 0.2289
## Detection Prevalence : 0.3133
## Balanced Accuracy : 0.9194
##
## 'Positive' Class : Yes
## # 9. Extract and store Accuracy, Precision, Recall, and F1-score
accuracy_svm <- conf_matrix_svm$overall['Accuracy']
precision_svm <- conf_matrix_svm$byClass['Pos Pred Value']
recall_svm <- conf_matrix_svm$byClass['Sensitivity']
f1_score_svm <- conf_matrix_svm$byClass['F1']
# 10. Calculate the AUC
# Ensure there are enough unique values in df_test_cleaned$tsunami to compute AUC
if (length(unique(df_test_cleaned$tsunami)) > 1) {
# Remove NA values from probabilities and actual labels for AUC calculation consistency
valid_indices <- !is.na(svm_probabilities) & !is.na(df_test_cleaned$tsunami)
roc_obj_svm <- roc(response = df_test_cleaned$tsunami[valid_indices],
predictor = svm_probabilities[valid_indices],
levels = levels(df_test_cleaned$tsunami))
auc_value_svm <- auc(roc_obj_svm)
} else {
auc_value_svm <- NA # Cannot compute AUC if only one class is present in the test set
}## Setting direction: controls < cases# 11. Print all calculated metrics
cat("\n--- Support Vector Machine (SVM) Model Performance on Test Set ---\n")##
## --- Support Vector Machine (SVM) Model Performance on Test Set ---cat("Accuracy:", accuracy_svm, "\n")## Accuracy: 0.9036145cat("Precision (for Tsunami=", positive_class_name, "):", precision_svm, "\n")## Precision (for Tsunami= Yes ): 0.7307692cat("Recall (for Tsunami=", positive_class_name, "):", recall_svm, "\n")## Recall (for Tsunami= Yes ): 0.95cat("F1-Score (for Tsunami=", positive_class_name, "):", f1_score_svm, "\n")## F1-Score (for Tsunami= Yes ): 0.826087cat("AUC:", auc_value_svm, "\n")## AUC: 0.9642857# 12. Store these metrics in a list for later comparison
svm_metrics <- list(
Model = "Support Vector Machine",
Accuracy = accuracy_svm,
Precision = precision_svm,
Recall = recall_svm,
F1_Score = f1_score_svm,
AUC = auc_value_svm
)
cat("\n--- Stored SVM Metrics ---\n")##
## --- Stored SVM Metrics ---print(svm_metrics)## $Model
## [1] "Support Vector Machine"
##
## $Accuracy
## Accuracy
## 0.9036145
##
## $Precision
## Pos Pred Value
## 0.7307692
##
## $Recall
## Sensitivity
## 0.95
##
## $F1_Score
## F1
## 0.826087
##
## $AUC
## Area under the curve: 0.9643Discussion of Support Vector Machine (SVM) Model Performance
The Support Vector Machine (SVM) model demonstrates strong overall classification ability on the test set, achieving 90.36% accuracy with a high AUC of 0.964, which indicates excellent separability between tsunami and non-tsunami events. Despite its strong performance, the model shows a noticeable imbalance between recall (95%) and precision (73.08%), indicating a higher rate of false positives. Additionally, SVM models can be sensitive to the choice of kernel and hyperparameters, and their decision boundaries are less interpretable compared to simpler models, which may limit practical interpretability
Key Metrics Analysis:
| Metric | Score |
|---|---|
| Accuracy | 0.9036 |
| Precision | 0.7308 |
| Recall | 0.9500 |
| F1-Score | 0.8261 |
| AUC | 0.9643 |
Train and Evaluate Decision Tree Model
This code installs rpart and rpart.plot only to ensure the required packages for building and visualizing decision trees are loaded without causing errors.
# Install rpart if not already installed
if (!requireNamespace("rpart", quietly = TRUE)) {
install.packages("rpart", dependencies = TRUE)
}
library(rpart)
# Install rpart.plot if not already installed
if (!requireNamespace("rpart.plot", quietly = TRUE)) {
install.packages("rpart.plot", dependencies = TRUE)
}
library(rpart.plot)
print("rpart and rpart.plot packages installed and loaded successfully.")## [1] "rpart and rpart.plot packages installed and loaded successfully."Train and Evaluate Decision Tree Model
I train a Decision Tree model, make predictions, and evaluate its performance. This involves preparing the data, training the model, making both class and probability predictions and calculating various metrics (accuracy, precision, recall, F1-score, AUC)
# 1. Ensure the caret and pROC packages are loaded. If not, install and load them.
# These should already be loaded from previous steps, but included for robustness.
if (!requireNamespace("caret", quietly = TRUE)) {
install.packages("caret", dependencies = TRUE)
}
if (!requireNamespace("pROC", quietly = TRUE)) {
install.packages("pROC", dependencies = TRUE)
}
library(caret)
library(pROC)
library(rpart) # Already loaded in the previous step
library(rpart.plot) # Already loaded in the previous step# 2. Sanitize column names in both df_train and df_test
# This step was done in previous blocks, but re-executing for robustness.
names(df_train) <- make.names(names(df_train), unique = TRUE)
names(df_test) <- make.names(names(df_test), unique = TRUE)
# 3. Ensure the levels of the 'tsunami' factor are consistent and valid
# This step was done in previous blocks, but re-executing for robustness.
levels(df_train$tsunami) <- make.names(levels(df_train$tsunami))
levels(df_test$tsunami) <- make.names(levels(df_test$tsunami))
# 4. Identify the positive class
positive_class_name <- "Yes"
# 5. Prepare cleaned versions of df_train and df_test by removing NA rows
# This ensures consistency with how other models handled NAs.
df_train_cleaned <- na.omit(df_train)
df_test_cleaned <- na.omit(df_test)
# 6. Train a Decision Tree model
cat("\n--- Training Decision Tree Model ---\n")##
## --- Training Decision Tree Model ---dt_model <- rpart(tsunami ~ ., data = df_train_cleaned, method = "class", na.action = na.omit)
# 7. Make class predictions on the cleaned df_test set
dt_predictions <- predict(dt_model, newdata = df_test_cleaned, type = 'class')
# 8. Make probability predictions on the cleaned df_test set
dt_probabilities_raw <- predict(dt_model, newdata = df_test_cleaned, type = 'prob')
dt_probabilities <- dt_probabilities_raw[, positive_class_name]
# 9. Create a confusion matrix
cat("\n--- Confusion Matrix for Decision Tree Model ---\n")##
## --- Confusion Matrix for Decision Tree Model ---conf_matrix_dt <- confusionMatrix(dt_predictions, df_test_cleaned$tsunami, positive = positive_class_name)
print(conf_matrix_dt)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 58 0
## Yes 5 20
##
## Accuracy : 0.9398
## 95% CI : (0.865, 0.9802)
## No Information Rate : 0.759
## P-Value [Acc > NIR] : 1.333e-05
##
## Kappa : 0.8483
##
## Mcnemar's Test P-Value : 0.07364
##
## Sensitivity : 1.0000
## Specificity : 0.9206
## Pos Pred Value : 0.8000
## Neg Pred Value : 1.0000
## Prevalence : 0.2410
## Detection Rate : 0.2410
## Detection Prevalence : 0.3012
## Balanced Accuracy : 0.9603
##
## 'Positive' Class : Yes
## # 10. Extract Accuracy, Precision, Recall, and F1-score
accuracy_dt <- conf_matrix_dt$overall['Accuracy']
precision_dt <- conf_matrix_dt$byClass['Pos Pred Value']
recall_dt <- conf_matrix_dt$byClass['Sensitivity']
f1_score_dt <- conf_matrix_dt$byClass['F1']
# 11. Calculate the AUC
# Ensure there are enough unique values in df_test_cleaned$tsunami to compute AUC
if (length(unique(df_test_cleaned$tsunami)) > 1) {
# Remove NA values from probabilities and actual labels for AUC calculation consistency
valid_indices <- !is.na(dt_probabilities) & !is.na(df_test_cleaned$tsunami)
roc_obj_dt <- roc(response = df_test_cleaned$tsunami[valid_indices],
predictor = dt_probabilities[valid_indices],
levels = levels(df_test_cleaned$tsunami))
auc_value_dt <- auc(roc_obj_dt)
} else {
auc_value_dt <- NA # Cannot compute AUC if only one class is present in the test set
}## Setting direction: controls < cases# 12. Print all calculated metrics
cat("\n--- Decision Tree Model Performance on Test Set ---\n")##
## --- Decision Tree Model Performance on Test Set ---cat("Accuracy:", accuracy_dt, "\n")## Accuracy: 0.939759cat("Precision (for Tsunami=", positive_class_name, "):", precision_dt, "\n")## Precision (for Tsunami= Yes ): 0.8cat("Recall (for Tsunami=", positive_class_name, "):", recall_dt, "\n")## Recall (for Tsunami= Yes ): 1cat("F1-Score (for Tsunami=", positive_class_name, "):", f1_score_dt, "\n")## F1-Score (for Tsunami= Yes ): 0.8888889cat("AUC:", auc_value_dt, "\n")## AUC: 0.9702381# 13. Store these metrics in a list for later comparison
dt_metrics <- list(
Model = "Decision Tree",
Accuracy = accuracy_dt,
Precision = precision_dt,
Recall = recall_dt,
F1_Score = f1_score_dt,
AUC = auc_value_dt
)
cat("\n--- Stored Decision Tree Metrics ---\n")##
## --- Stored Decision Tree Metrics ---print(dt_metrics)## $Model
## [1] "Decision Tree"
##
## $Accuracy
## Accuracy
## 0.939759
##
## $Precision
## Pos Pred Value
## 0.8
##
## $Recall
## Sensitivity
## 1
##
## $F1_Score
## F1
## 0.8888889
##
## $AUC
## Area under the curve: 0.9702Discussion of Decision Tree Model Performance
The model achieved a high accuracy of 93.98% on the test set. A recall of 1.00 indicates that all tsunami events were correctly identified, but the lower precision of 0.80 suggests the presence of false positives. This imbalance implies the model prioritizes detecting tsunami cases at the expense of over-predicting them. While the high AUC (0.97) reflects strong class separation, such performance may also indicate potential overfitting, especially if the dataset is small or imbalanced.
Key Metrics Analysis
| Metric | Score |
|---|---|
| Accuracy | 0.9398 |
| Precision | 0.8000 |
| Recall | 1.0000 |
| F1-Score | 0.8889 |
| AUC | 0.9702 |
Train and Evaluate K-Nearest Neighbors (KNN) Model
I need to ensure the required R packages for KNN modeling and
evaluation are installed and loaded. This includes class
for the knn function, and caret and
pROC for evaluation metrics. Although these might have been
loaded previously, re-checking ensures robustness for this specific
task.
# 1. Install and load necessary packages
if (!requireNamespace("class", quietly = TRUE)) {
install.packages("class", dependencies = TRUE)
}
library(class)
if (!requireNamespace("caret", quietly = TRUE)) {
install.packages("caret", dependencies = TRUE)
}
library(caret)
if (!requireNamespace("pROC", quietly = TRUE)) {
install.packages("pROC", dependencies = TRUE)
}
library(pROC)
print("Required R packages (class, caret, pROC) loaded successfully.")## [1] "Required R packages (class, caret, pROC) loaded successfully."I train the K-Nearest Neighbors model, make predictions, and evaluate its performance based on the provided instructions. This involves preparing the data, training the model, making both class and probability predictions,and calculating various metrics (accuracy, precision, recall, F1-score, AUC)
# 2. Sanitize column names in both df_train and df_test
# This step was done in previous blocks, but re-executing for robustness.
names(df_train) <- make.names(names(df_train), unique = TRUE)
names(df_test) <- make.names(names(df_test), unique = TRUE)
# 3. Ensure the levels of the 'tsunami' factor are consistent and valid
# This step was done in previous blocks, but re-executing for robustness.
levels(df_train$tsunami) <- make.names(levels(df_train$tsunami))
levels(df_test$tsunami) <- make.names(levels(df_test$tsunami))
# 4. Identify the positive class
positive_class_name <- "Yes"
# 5. Prepare cleaned versions of df_train and df_test by removing NA rows
# This ensures consistency with how other models handled NAs.
df_train_cleaned <- na.omit(df_train)
df_test_cleaned <- na.omit(df_test)
# 6. Extract the feature matrix (excluding the 'tsunami' column) and the target vector ('tsunami' column)
x_train <- df_train_cleaned %>% select(-tsunami)
y_train <- df_train_cleaned$tsunami
x_test <- df_test_cleaned %>% select(-tsunami)
y_test <- df_test_cleaned$tsunami
# Ensure all feature columns are numeric for KNN
# This is important as knn expects numeric input
x_train_numeric <- as.data.frame(lapply(x_train, as.numeric))
x_test_numeric <- as.data.frame(lapply(x_test, as.numeric))
# 7. Train a K-Nearest Neighbors model using the knn function with k=5
cat("\n--- Training K-Nearest Neighbors Model ---\n")##
## --- Training K-Nearest Neighbors Model ---# The 'knn' function doesn't 'train' a model in the traditional sense,
# but rather performs the classification directly. We will get class predictions and probabilities.
# Make class predictions
knn_predictions_with_prob <- knn(
train = x_train_numeric,
test = x_test_numeric,
cl = y_train,
k = 5,
prob = TRUE # To get probabilities
)
# Extract class predictions
knn_predictions <- as.factor(as.character(knn_predictions_with_prob))
levels(knn_predictions) <- levels(y_test) # Ensure levels match for confusionMatrix
# Extract probabilities for the positive class
# The 'prob' attribute stores the proportion of the votes for the winning class.
# We need to correctly assign this probability to the positive class.
knn_probabilities <- ifelse(knn_predictions_with_prob == positive_class_name,
attr(knn_predictions_with_prob, "prob"),
1 - attr(knn_predictions_with_prob, "prob"))
# If the class prediction is 'No', the reported probability is 1-prob, so we need to adjust.
# The 'prob' attribute is for the *predicted* class.
# We need the probability for the 'Yes' class specifically.
# Let's re-calculate probabilities more explicitly:
# knn.cv() for cross-validation, but knn() directly for prediction.
# To get probabilities for both classes, one common approach for knn is to get the votes
# or use caret::train which handles this better.
# For simple knn, 'prob' gives the proportion of votes for the predicted class.
# If the predicted class is 'Yes', prob is P(Yes). If 'No', prob is P(No).
# We need P(Yes) for AUC calculation. So, if predicted class is 'No', P(Yes) = 1 - prob.
knn_probabilities_for_auc <- numeric(length(knn_predictions_with_prob))
for (i in seq_along(knn_predictions_with_prob)) {
if (knn_predictions_with_prob[i] == positive_class_name) {
knn_probabilities_for_auc[i] <- attr(knn_predictions_with_prob, "prob")[i]
} else {
knn_probabilities_for_auc[i] <- 1 - attr(knn_predictions_with_prob, "prob")[i]
}
}
# 9. Create a confusion matrix
cat("\n--- Confusion Matrix for K-Nearest Neighbors Model ---\n")##
## --- Confusion Matrix for K-Nearest Neighbors Model ---conf_matrix_knn <- confusionMatrix(knn_predictions, y_test, positive = positive_class_name)
print(conf_matrix_knn)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 62 16
## Yes 1 4
##
## Accuracy : 0.7952
## 95% CI : (0.6924, 0.8759)
## No Information Rate : 0.759
## P-Value [Acc > NIR] : 0.264992
##
## Kappa : 0.2475
##
## Mcnemar's Test P-Value : 0.000685
##
## Sensitivity : 0.20000
## Specificity : 0.98413
## Pos Pred Value : 0.80000
## Neg Pred Value : 0.79487
## Prevalence : 0.24096
## Detection Rate : 0.04819
## Detection Prevalence : 0.06024
## Balanced Accuracy : 0.59206
##
## 'Positive' Class : Yes
## # 10. Extract Accuracy, Precision, Recall, and F1-score
accuracy_knn <- conf_matrix_knn$overall['Accuracy']
precision_knn <- conf_matrix_knn$byClass['Pos Pred Value']
recall_knn <- conf_matrix_knn$byClass['Sensitivity']
f1_score_knn <- conf_matrix_knn$byClass['F1']
# 11. Calculate the AUC
# Ensure there are enough unique values in y_test to compute AUC
if (length(unique(y_test)) > 1) {
# Remove NA values from probabilities and actual labels for AUC calculation consistency
valid_indices <- !is.na(knn_probabilities_for_auc) & !is.na(y_test)
roc_obj_knn <- roc(response = y_test[valid_indices],
predictor = knn_probabilities_for_auc[valid_indices],
levels = levels(y_test))
auc_value_knn <- auc(roc_obj_knn)
} else {
auc_value_knn <- NA # Cannot compute AUC if only one class is present in the test set
}## Setting direction: controls < cases# 12. Print all calculated metrics
cat("\n--- K-Nearest Neighbors Model Performance on Test Set ---\n")##
## --- K-Nearest Neighbors Model Performance on Test Set ---cat("Accuracy:", accuracy_knn, "\n")## Accuracy: 0.7951807cat("Precision (for Tsunami=", positive_class_name, "):", precision_knn, "\n")## Precision (for Tsunami= Yes ): 0.8cat("Recall (for Tsunami=", positive_class_name, "):", recall_knn, "\n")## Recall (for Tsunami= Yes ): 0.2cat("F1-Score (for Tsunami=", positive_class_name, "):", f1_score_knn, "\n")## F1-Score (for Tsunami= Yes ): 0.32cat("AUC:", auc_value_knn, "\n")## AUC: 0.8071429# 13. Store these metrics in a list for later comparison
knn_metrics <- list(
Model = "K-Nearest Neighbors",
Accuracy = accuracy_knn,
Precision = precision_knn,
Recall = recall_knn,
F1_Score = f1_score_knn,
AUC = auc_value_knn
)
cat("\n--- Stored KNN Metrics ---\n")##
## --- Stored KNN Metrics ---print(knn_metrics)## $Model
## [1] "K-Nearest Neighbors"
##
## $Accuracy
## Accuracy
## 0.7951807
##
## $Precision
## Pos Pred Value
## 0.8
##
## $Recall
## Sensitivity
## 0.2
##
## $F1_Score
## F1
## 0.32
##
## $AUC
## Area under the curve: 0.8071Discussion of K-Nearest Neighbors (KNN) Model Performance
The K-Nearest Neighbors model achieved an accuracy of 79.52% on the test set, but its performance on the positive class (tsunami events) is weak. The recall of 0.20 indicates that the model missed most actual tsunami cases, despite a high precision of 0.80, meaning few false positives when it does predict a tsunami. The low Kappa value (0.2475) suggests only fair agreement beyond chance, highlighting limited reliability. Although the AUC (0.81) indicates moderate class separability, the model’s low sensitivity makes it unsuitable for applications where detecting tsunami events is critical.
Key Metrics Analysis
| Metric | Score |
|---|---|
| Accuracy | 0.7952 |
| Precision | 0.8000 |
| Recall | 0.2000 |
| F1-Score | 0.3200 |
| AUC | 0.8071 |
Consolidate All Model Metrics
To consolidate all model metrics, I will extract the performance
metrics from each model’s evaluation results. Since the initial Random
Forest model’s metrics were not stored in a list like the others, I will
re-calculate them using the df_test_cleaned data frame and
then combine all the metrics into a single R data frame for
comparison.
# Ensure required packages are loaded for re-evaluation if necessary
library(caret)
library(pROC)
# Ensure df_test_cleaned is available and levels are consistent
# These steps are for robustness, assuming they were handled in previous cells
names(df_test) <- make.names(names(df_test), unique = TRUE)
levels(df_test$tsunami) <- make.names(levels(df_test$tsunami))
df_test_cleaned <- na.omit(df_test)
positive_class_name <- "Yes"
# --- 1. Extract metrics for Initial Random Forest Model ---
# Make predictions on the df_test_cleaned dataset using the initial model
rf_predictions_initial <- predict(rf_model_initial, newdata = df_test_cleaned, type = 'class')
rf_probabilities_initial <- predict(rf_model_initial, newdata = df_test_cleaned, type = 'prob')
# Ensure levels are consistent, positive class is "Yes"
# The initial model was trained on levels 0 and 1. We re-map predictions to "No" and "Yes"
# for confusionMatrix, but probabilities retain original column names (0, 1).
levels(rf_predictions_initial) <- levels(df_test_cleaned$tsunami)
# Create a confusion matrix for the initial model
conf_matrix_initial <- confusionMatrix(rf_predictions_initial, df_test_cleaned$tsunami, positive = positive_class_name)
# Calculate AUC for the initial model
# Use the probability column '1' from rf_probabilities_initial (which corresponds to 'Yes' class)
roc_obj_initial <- roc(response = df_test_cleaned$tsunami,
predictor = rf_probabilities_initial[, "1"], # Access column "1"
levels = levels(df_test_cleaned$tsunami),
positive = positive_class_name)## Setting direction: controls < casesauc_value_initial <- auc(roc_obj_initial)
initial_rf_metrics_list <- data.frame(
Model = "Initial Random Forest",
Accuracy = conf_matrix_initial$overall['Accuracy'],
Precision = conf_matrix_initial$byClass['Pos Pred Value'],
Recall = conf_matrix_initial$byClass['Sensitivity'],
F1_Score = conf_matrix_initial$byClass['F1'],
AUC = auc_value_initial
)
# --- 2. Extract metrics for Tuned Random Forest Model ---
# Metrics are available from conf_matrix_tuned and auc_value_tuned from previous step
tuned_rf_metrics_list <- data.frame(
Model = "Tuned Random Forest",
Accuracy = conf_matrix_tuned$overall['Accuracy'],
Precision = conf_matrix_tuned$byClass['Pos Pred Value'],
Recall = conf_matrix_tuned$byClass['Sensitivity'],
F1_Score = conf_matrix_tuned$byClass['F1'],
AUC = auc_value_tuned
)
# --- 3. Consolidate metrics from other models (already stored as lists) ---
# Convert the previously stored lists to data frames for consistency
log_reg_df <- as.data.frame(log_reg_metrics)
svm_df <- as.data.frame(svm_metrics)
dt_df <- as.data.frame(dt_metrics)
knn_df <- as.data.frame(knn_metrics)
# Ensure column names are consistent before combining
colnames(log_reg_df) <- c("Model", "Accuracy", "Precision", "Recall", "F1_Score", "AUC")
colnames(svm_df) <- c("Model", "Accuracy", "Precision", "Recall", "F1_Score", "AUC")
colnames(dt_df) <- c("Model", "Accuracy", "Precision", "Recall", "F1_Score", "AUC")
colnames(knn_df) <- c("Model", "Accuracy", "Precision", "Recall", "F1_Score", "AUC")
# Combine all metrics into a single data frame
all_model_metrics <- rbind(
initial_rf_metrics_list,
tuned_rf_metrics_list,
log_reg_df,
svm_df,
dt_df,
knn_df
)
# Print the consolidated data frame
cat("\n--- Consolidated Model Performance Metrics ---\n")##
## --- Consolidated Model Performance Metrics ---print(all_model_metrics)## Model Accuracy Precision Recall F1_Score AUC
## Accuracy Initial Random Forest 0.9397590 0.8260870 0.95 0.8837209 0.9968254
## Accuracy1 Tuned Random Forest 0.9759036 0.9500000 0.95 0.9500000 0.9960317
## Accuracy2 Logistic Regression 0.8072289 0.5833333 0.70 0.6363636 0.8027778
## Accuracy3 Support Vector Machine 0.9036145 0.7307692 0.95 0.8260870 0.9642857
## Accuracy4 Decision Tree 0.9397590 0.8000000 1.00 0.8888889 0.9702381
## Accuracy5 K-Nearest Neighbors 0.7951807 0.8000000 0.20 0.3200000 0.8071429Results
Key Metrics of all model score
| Model | Accuracy | Precision | Recall | F1_Score | AUC |
|---|---|---|---|---|---|
| Initial Random Forest | 0.939759 | 0.826087 | 0.95 | 0.883721 | 0.996825 |
| Tuned Random Forest | 0.975904 | 0.95 | 0.95 | 0.95 | 0.996032 |
| Logistic Regression | 0.807229 | 0.583333 | 0.70 | 0.636364 | 0.802778 |
| Support Vector Machine | 0.903615 | 0.730769 | 0.95 | 0.826087 | 0.964286 |
| Decision Tree | 0.939759 | 0.80 | 1.00 | 0.888889 | 0.970238 |
| K-Nearest Neighbors | 0.795181 | 0.80 | 0.20 | 0.32 | 0.807143 |
Visualize Model Comparisons (Accuracy)
A bar chart comparing the Accuracy of all models (baseline RF, tuned RF, Logistic Regression, SVM, Decision Tree, KNN).
# Ensure ggplot2 is loaded (should be from previous EDA steps)
if (!require(ggplot2)) {
install.packages("ggplot2", repos = "http://cran.us.r-project.org")
}
library(ggplot2)
# Create a bar chart comparing Accuracy of all models
ggplot(all_model_metrics, aes(x = Model, y = Accuracy, fill = Model)) +
geom_bar(stat = "identity", color = "black") +
labs(
title = "Model Comparison: Accuracy",
x = "Model",
y = "Accuracy"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
legend.position = "none" # Remove legend as fill is redundant with x-axis
)
Tuned Random Forest: Accuracy and Project Impact
The Tuned Random Forest model achieved a remarkable Accuracy of 97.59% on the test set. Accuracy, in general, measures the proportion of correctly classified instances (both actual tsunami events correctly predicted, and non-tsunami events correctly predicted) out of the total number of predictions made by the model.
How this impacts the project:
- High Reliability: An accuracy of nearly 98% suggests that the model is highly reliable in its overall classifications. For a critical task like tsunami prediction, high accuracy is paramount, as misclassifications can have severe consequences.
- Effective Generalization: This high accuracy on unseen test data indicates that the model has learned meaningful patterns from the training data and can effectively generalize its predictions to new, real-world earthquake events. This is crucial for its practical deployment.
- Confidence in Predictions: When paired with its
high precision (0.95), recall (0.95), F1-score (0.95), and an
exceptional AUC (0.996), the high accuracy reinforces confidence in the
model’s ability to not only identify tsunami-generating earthquakes but
also to avoid false alarms. This balance is critical because:
- High Recall (0.95) means it correctly identifies most actual tsunami events, minimizing the risk of missed warnings.
- High Precision (0.95) means that when it predicts a tsunami, it is very likely to be correct, reducing unnecessary evacuations or alerts.
In the context of this project, which aims to predict tsunami risk, the Tuned Random Forest’s high accuracy, combined with its strong performance across other key metrics, makes it an exceptionally valuable tool. It suggests that the model can be effectively used to inform early warning systems, aid disaster preparedness, and ultimately help mitigate the impact of tsunamis by providing timely and accurate predictions.
Conclusion
This project developed and evaluated a series of supervised machine learning models for tsunami event prediction based on earthquake-related features. By systematically training, tuning, and validating multiple algorithms—including Random Forest (initial and tuned), Logistic Regression, Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbors (KNN)—the study aimed to identify an optimal predictive framework balancing accuracy, recall, and interpretability.
Across all models, the tuned Random Forest emerged as the most robust and balanced performer, achieving high accuracy, precision, recall, and AUC, thus demonstrating strong discriminative capacity and generalization potential. Its ability to correctly identify nearly all tsunami-generating earthquakes while minimizing false alarms highlights its suitability for real-world early warning applications. The Decision Tree model also performed admirably, showing perfect recall, indicating complete sensitivity to tsunami occurrences—though at the cost of slightly higher false positives. The SVM demonstrated consistently high classification accuracy but exhibited minor imbalance between precision and recall, implying limited reliability in uncertain boundary cases. Logistic Regression and KNN, while conceptually simpler, showed reduced predictive strength and sensitivity, particularly KNN, which struggled to detect true tsunami cases effectively.
The integrated model comparison confirmed that ensemble-based approaches (particularly the tuned Random Forest) significantly outperformed linear and instance-based methods, suggesting that complex, nonlinear interactions among seismic variables are key to accurate tsunami risk modeling. Visual analyses, including ROC curves and actual-versus-predicted charts, further substantiated the Random Forest’s dominance in maintaining high sensitivity and specificity simultaneously.
In summary, this project established a reproducible, data-driven workflow for tsunami occurrence prediction. It demonstrated that ensemble learning, when properly tuned and evaluated using comprehensive metrics, can serve as a reliable foundation for automated tsunami early warning systems. These findings not only contribute to improved disaster preparedness and mitigation strategies but also offer a methodological reference for future geohazard classification research
References
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Blaser, L., Ohrnberger, M., Riggelsen, C., Babeyko, A. Y., & Scherbaum, F. (2011). Bayesian networks for tsunami early warning. Geophysical Journal International, 185(3), 1431–1443. https://doi.org/10.1111/j.1365-246X.2011.05020.x
Cesario, E., Giampá, S., Baglione, E., Cordrie, L., Selva, J., & Talia, D. (2024). Machine learning for tsunami waves forecasting using regression trees. Big Data Research, 36, 100452. https://doi.org/10.1016/j.bdr.2024.100452
Mulia, I. E., Ueda, N., Miyoshi, T., Gusman, A. R., & Satake, K. (2022). Machine learning-based tsunami inundation prediction derived from offshore observations. Nature Communications, 13, 5489. https://doi.org/10.1038/s41467-022-33253-5
Selva, J., Lorito, S., Volpe, M., Romano, F., Tonini, R., Perfetti, P., … Amato, A. (2021). Probabilistic tsunami forecasting for early warning. Nature Communications, 12(1), 5677. https://doi.org/10.1038/s41467-021-25815-w