Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment.

Viet-Ha Nhu,Ayub Mohammadi,Wei Chen,John J Clague,Nadhir Al-Ansari,Himan Shahabi,Baharin Bin Ahmad,Hoang Nguyen,Ataollah Shirzadi,Abolfazl Jaafari

doi:10.3390/ijerph17144933

Abstract

We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

Highlights

Landslides are the slow to rapid downslope movement of Earth materials triggered by a wide variety of natural processes, as well as by land surface disturbances due to human activities [1,2,3,4,5,6].Whether triggered naturally or by human activities, landslides are responsible for much economic damage and loss of life each year [7,8,9]
We developed three machine learning models (i.e., AB, alternating decision tree (ADTree), and AB-ADTree) to perform the landslide susceptibility mapping
All 17 landslide conditioning factors used in this study are deemed to be important, because they have positive values of average merit based on the One Rule (One-R) technique

Summary

Introduction

Whether triggered naturally or by human activities, landslides are responsible for much economic damage and loss of life each year [7,8,9]. Recurrent landslides along roads and on other cut slopes in mountainous regions pose a great threat to the people living in these areas [3,5,10,11,12]. In Malaysia, landslides pose a constant threat to infrastructure, agriculture, other natural resources, and tourism, and local and central governments are strained financially and logistically in dealing with them [8,13]. One area in the country that is impacted by landslides, due in part to increased urbanization and expanded plantation agriculture, is the Cameron Highlands in the central part of peninsular Malaysia [6,8]

Methods

Results

Discussion

Conclusion