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
收藏
页数:13
相关论文
共 50 条
  • [41] Landslide susceptibility along National Highway-7 in the Himalayas using random forest-based machine learning tool
    Gupta, Khyati
    Yunus, Ali P.
    Siddique, Tariq
    Ahamad, Atif
    JOURNAL OF EARTH SYSTEM SCIENCE, 2025, 134 (02)
  • [42] PRECIPITATION-TRIGGERED LANDSLIDE PREDICTION IN NEPAL USING MACHINE LEARNING AND DEEP LEARNING
    Doerksen, Kelsey
    Gal, Yarin
    Kalaitzis, Freddie
    Rossi, Cristian
    Petit, David
    Li, Sihan
    Dadson, Simon
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 4962 - 4965
  • [43] A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
    Huang, Faming
    Zhang, Jing
    Zhou, Chuangbing
    Wang, Yuhao
    Huang, Jinsong
    Zhu, Li
    LANDSLIDES, 2020, 17 (01) : 217 - 229
  • [44] A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
    Faming Huang
    Jing Zhang
    Chuangbing Zhou
    Yuhao Wang
    Jinsong Huang
    Li Zhu
    Landslides, 2020, 17 : 217 - 229
  • [45] Landslide susceptibility mapping by using a geographic information system (GIS) along the China-Pakistan Economic Corridor (Karakoram Highway), Pakistan
    Ali, Sajid
    Biermanns, Peter
    Haider, Rashid
    Reicherter, Klaus
    NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2019, 19 (05) : 999 - 1022
  • [46] Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China
    Guo, Changbao
    Montgomery, David R.
    Zhang, Yongshuang
    Wang, Ke
    Yang, Zhihua
    GEOMORPHOLOGY, 2015, 248 : 93 - 110
  • [47] Comparative analysis of the TabNet algorithm and traditional machine learning algorithms for landslide susceptibility assessment in the Wanzhou Region of China
    Yingze, Song
    Yingxu, Song
    Xin, Zhang
    Jie, Zhou
    Degang, Yang
    NATURAL HAZARDS, 2024, 120 (08) : 7627 - 7652
  • [48] Regional landslide susceptibility assessment using multi-stage remote sensing data along the coastal range highway in northeastern Taiwan
    Lee, Ching-Fang
    Huang, Wei-Kai
    Chang, Yu-Lin
    Chi, Shu-Yeong
    Liao, Wu-Chang
    GEOMORPHOLOGY, 2018, 300 : 113 - 127
  • [49] Landslide susceptibility assessment using the certainty factor and deep neural network
    Ma, Wenli
    Dong, Jianhui
    Wei, Zhanxi
    Peng, Liang
    Wu, Qihong
    Wang, Xiao
    Dong, Yangdan
    Wu, Yuanzao
    FRONTIERS IN EARTH SCIENCE, 2023, 10
  • [50] Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms
    Huang, Wubiao
    Ding, Mingtao
    Li, Zhenhong
    Yu, Junchuan
    Ge, Daqing
    Liu, Qi
    Yang, Jing
    CATENA, 2023, 222