Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway.

Liming Xiao,Gongzhuang Peng,Yonghong Zhang

doi:10.3390/s18124436

Abstract

The China-Nepal Highway is a vital land route in the Kush-Himalayan region. The occurrence of mountain hazards in this area is a matter of serious concern. Thus, it is of great importance to perform hazard assessments in a more accurate and real-time way. Based on temporal and spatial sensor data, this study tries to use data-driven algorithms to predict landslide susceptibility. Ten landslide instability factors were prepared, including elevation, slope angle, slope aspect, plan curvature, vegetation index, built-up index, stream power, lithology, precipitation intensity, and cumulative precipitation index. Four machine learning algorithms, namely decision tree (DT), support vector machines (SVM), Back Propagation neural network (BPNN), and Long Short Term Memory (LSTM) are implemented, and their final prediction accuracies are compared. The experimental results showed that the prediction accuracies of BPNN, SVM, DT, and LSTM in the test areas are 62.0%, 72.9%, 60.4%, and 81.2%, respectively. LSTM outperformed the other three models due to its capability to learn time series with long temporal dependencies. It indicates that the dynamic change course of geological and geographic parameters is an important indicator in reflecting landslide susceptibility.

Highlights

The China-Nepal Highway is a vital land route connecting China and Nepal, which is an important part of the “One Belt and One Road” development strategy
The experimental results showed that the prediction accuracies of Back Propagation neural network (BPNN), support vector machines (SVM), decision tree (DT), and Long Short Term Memory (LSTM) in the test areas are 62.0%, 72.9%, 60.4%, and 81.2%, respectively
A total of 3800 data points collected from the monitoring site during the period from January of 3800 2016 data points collected from the monitoring siteisduring

Summary

Introduction

The China-Nepal Highway is a vital land route connecting China and Nepal, which is an important part of the “One Belt and One Road” development strategy. Due to the fragile ecological environment and highly-varying hydrothermal conditions, mountain hazards such as landslides and mudslides take place frequently and have caused severe damage to infrastructure. It is of great importance to perform the mountain hazard assessment in a more accurate and real-time way. Taking landslide related hazards as the research object, a prediction model is established to assess the susceptibility in this paper. Apart from being time-consuming and strenuous, the traditional method has a limitation in that the measurement process lacks of accuracy and depends heavily on experts’

Objectives

Results

Discussion

Conclusion