Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
The current study aimed at evaluating the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear Dis...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-03-01
|
Series: | Geoscience Frontiers |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987120301298 |
id |
doaj-3a3b1711973040f5860a5b470e1bcf29 |
---|---|
record_format |
Article |
spelling |
doaj-3a3b1711973040f5860a5b470e1bcf292021-01-26T04:12:05ZengElsevierGeoscience Frontiers1674-98712021-03-01122639655Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi ArabiaAhmed Mohamed Youssef0Hamid Reza Pourghasemi1Geology Department, Faculty of Science, Sohag University, Egypt; Geological Hazards Department, Applied Geology Sector, Saudi Geological Survey, P.O. Box 54141, Jeddah, 21514, Saudi ArabiaDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran; Corresponding author.The current study aimed at evaluating the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Naive Bayes (NB), for landslide susceptibility modeling and comparison of their performances. Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue. This study was carried out using GIS and R open source software at Abha Basin, Asir Region, Saudi Arabia. First, a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources. All the landslide areas were randomly separated into two groups with a ratio of 70% for training and 30% for validating purposes. Twelve landslide-variables were generated for landslide susceptibility modeling, which include altitude, lithology, distance to faults, normalized difference vegetation index (NDVI), landuse/landcover (LULC), distance to roads, slope angle, distance to streams, profile curvature, plan curvature, slope length (LS), and slope-aspect. The area under curve (AUC-ROC) approach has been applied to evaluate, validate, and compare the MLTs performance. The results indicated that AUC values for seven MLTs range from 89.0% for QDA to 95.1% for RF. Our findings showed that the RF (AUC = 95.1%) and LDA (AUC = 941.7%) have produced the best performances in comparison to other MLTs. The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.http://www.sciencedirect.com/science/article/pii/S1674987120301298Landslide susceptibilityMachine learning algorithmsVariables importanceSaudi Arabia |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ahmed Mohamed Youssef Hamid Reza Pourghasemi |
spellingShingle |
Ahmed Mohamed Youssef Hamid Reza Pourghasemi Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia Geoscience Frontiers Landslide susceptibility Machine learning algorithms Variables importance Saudi Arabia |
author_facet |
Ahmed Mohamed Youssef Hamid Reza Pourghasemi |
author_sort |
Ahmed Mohamed Youssef |
title |
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia |
title_short |
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia |
title_full |
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia |
title_fullStr |
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia |
title_full_unstemmed |
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia |
title_sort |
landslide susceptibility mapping using machine learning algorithms and comparison of their performance at abha basin, asir region, saudi arabia |
publisher |
Elsevier |
series |
Geoscience Frontiers |
issn |
1674-9871 |
publishDate |
2021-03-01 |
description |
The current study aimed at evaluating the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Naive Bayes (NB), for landslide susceptibility modeling and comparison of their performances. Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue. This study was carried out using GIS and R open source software at Abha Basin, Asir Region, Saudi Arabia. First, a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources. All the landslide areas were randomly separated into two groups with a ratio of 70% for training and 30% for validating purposes. Twelve landslide-variables were generated for landslide susceptibility modeling, which include altitude, lithology, distance to faults, normalized difference vegetation index (NDVI), landuse/landcover (LULC), distance to roads, slope angle, distance to streams, profile curvature, plan curvature, slope length (LS), and slope-aspect. The area under curve (AUC-ROC) approach has been applied to evaluate, validate, and compare the MLTs performance. The results indicated that AUC values for seven MLTs range from 89.0% for QDA to 95.1% for RF. Our findings showed that the RF (AUC = 95.1%) and LDA (AUC = 941.7%) have produced the best performances in comparison to other MLTs. The outcome of this study and the landslide susceptibility maps would be useful for environmental protection. |
topic |
Landslide susceptibility Machine learning algorithms Variables importance Saudi Arabia |
url |
http://www.sciencedirect.com/science/article/pii/S1674987120301298 |
work_keys_str_mv |
AT ahmedmohamedyoussef landslidesusceptibilitymappingusingmachinelearningalgorithmsandcomparisonoftheirperformanceatabhabasinasirregionsaudiarabia AT hamidrezapourghasemi landslidesusceptibilitymappingusingmachinelearningalgorithmsandcomparisonoftheirperformanceatabhabasinasirregionsaudiarabia |
_version_ |
1724323438573649920 |