Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

Dieu Tien Bui,Himan Shahabi,Kamran Chapi,Wei Chen,Yingying Tian,Mohsen Alizadeh,Ayub Mohammadi,Haoyuan Hong,Mahdi Panahi,Baharin Ahmad,Ataollah Shirzadi

doi:10.3390/rs10101527

Abstract

Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.

Highlights

Natural disasters, such as landslides, floods, earthquakes, hurricanes, soil erosion and tsunamis, cause huge damages to properties and human lives, among which, landslides are known as one of the most important natural disasters worldwide [1], which are responsible for at least 17% of all natural hazard fatalities [2].In Southeast Asia, landslides are one of the most common disasters due to its special climate condition, mountainous terrain and socioeconomic circumstances [3]
The results indicated that the support vector machine (SVM) model had a higher goodness-of-fit and performance compared to the index of entropy (IOE) model
Landslides have frequently occurred in this area following heavy rainfall, in inaccessible areas where field work is difficult to carry out

Summary

Introduction

Natural disasters, such as landslides, floods, earthquakes, hurricanes, soil erosion and tsunamis, cause huge damages to properties and human lives, among which, landslides are known as one of the most important natural disasters worldwide [1], which are responsible for at least 17% of all natural hazard fatalities [2].In Southeast Asia, landslides are one of the most common disasters due to its special climate condition, mountainous terrain and socioeconomic circumstances [3]. Torrential rainfalls, which cause the heavy flow of mudslides, are the main trigger of landslides and their damages in Cameron Highlands area, Malaysia [4]. Though few landslides occurred in residential areas, in the Cameron Highlands, many of the landslides have occurred along roads and highways due to human interference (man-made/anthropogenic factor) and triggering factors such as heavy rainfall. This means that humans have prepared the conditions for landslides’ occurrence through the balance stability disturbance of natural slopes (no artificial slopes) [5]. Landslide susceptibility assessment can be achieved by providing accurate landslide information and accessible and continuous risk data [6]. It is of high necessity to obtain reliable landslide susceptibility maps using accurate data and new techniques in tropical areas for purposes such as implementing landslide mitigation measures [9]

Objectives

Methods

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Discussion

Conclusion