Paper Info
Title
Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway.
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.
Year
DOI
Venue
2018
10.3390/s18124436
SENSORS
Keywords
Field
DocType
landslide susceptibility,China-Nepal Highway,machine learning,LSTM,remote sensing images
Landslide susceptibility,China,Electronic engineering,Artificial intelligence,Engineering,Civil engineering,Deep learning
Journal
Volume
Issue
ISSN
18
12.0
1424-8220
Citations 
PageRank 
References 
3
0.37
0
Authors
3
Name
Order
Citations
PageRank
Liming Xiao130.37
Yonghong Zhang273.89
Gongzhuang Peng3155.41