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

被引:92
|
作者
Xiao, Liming [1 ]
Zhang, Yonghong [1 ]
Peng, Gongzhuang [2 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Dept Informat & Commun, Nanjing 210044, Jiangsu, Peoples R China
[2] Univ Sci & Technol Beijing, Engn Res Inst, Beijing 100083, Peoples R China
基金
美国国家科学基金会;
关键词
landslide susceptibility; China-Nepal Highway; machine learning; LSTM; remote sensing images; SUPPORT VECTOR MACHINE; POWER-LAW RELATIONSHIP; LOGISTIC-REGRESSION; NEURAL-NETWORKS; MODELS; AREA;
D O I
10.3390/s18124436
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
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页数:13
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