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Risk Mapping of Geological Hazards in Plateau Mountainous Areas Based on Multisource Remote Sensing Data Extraction and Machine Learning (Fuyuan, China)

Author

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  • Shaohan Zhang

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
    Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China)

  • Shucheng Tan

    (Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China
    School of Earth Science, Yunnan University, Kunming 650500, China)

  • Yongqi Sun

    (Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China)

  • Duanyu Ding

    (Faculty of Architecture and City Planning, Kunming University of Science and Technology, Kunming 650500, China)

  • Wei Yang

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
    Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China)

Abstract

Selecting the most effective prediction model and correctly identifying the main disaster-driving factors in a specific region are the keys to addressing the challenges of geological hazards. Fuyuan County is a typical plateau mountainous town, and slope geological hazards occur frequently. Therefore, it is highly important to study the spatial distribution characteristics of hazards in this area, explore machine learning models that can be highly matched with the geological environment of the study area, and improve the accuracy and reliability of the slope geological hazard risk zoning map (SGHRZM). This paper proposes a hazard mapping research method based on multisource remote sensing data extraction and machine learning. In this study, we visualize the risk level of geological hazards in the study area according to 10 pathogenic factors. Moreover, the accuracy of the disaster point list was verified on the spot. The results show that the coupling model can maximize the respective advantages of the models used and has highest mapping accuracy, and the area under the curve (AUC) is 0.923. The random forest (RF) model was the leader in terms of which single model performed best, with an AUC of 0.909. The grid search algorithm (GSA) is an efficient parameter optimization technique that can be used as a preferred method to improve the accuracy of a model. The list of disaster points extracted from remote sensing images is highly reliable. The high-precision coupling model and the single model have good adaptability in the study area. The research results can provide not only scientific references for local government departments to carry out disaster management work but also technical support for relevant research in surrounding mountainous towns.

Suggested Citation

  • Shaohan Zhang & Shucheng Tan & Yongqi Sun & Duanyu Ding & Wei Yang, 2024. "Risk Mapping of Geological Hazards in Plateau Mountainous Areas Based on Multisource Remote Sensing Data Extraction and Machine Learning (Fuyuan, China)," Land, MDPI, vol. 13(9), pages 1-25, August.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:9:p:1361-:d:1464009
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    References listed on IDEAS

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    1. Michael Makonyo & Zahor Zahor, 2023. "GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(2), pages 1085-1115, September.
    2. Junjie Ji & Yongzhang Zhou & Qiuming Cheng & Shoujun Jiang & Shiting Liu, 2023. "Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization," Land, MDPI, vol. 12(6), pages 1-22, May.
    3. Han Zhang & Chao Yin & Shaoping Wang & Bing Guo, 2023. "Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 1931-1971, March.
    4. Israr Ullah & Bilal Aslam & Syed Hassan Iqbal Ahmad Shah & Aqil Tariq & Shujing Qin & Muhammad Majeed & Hans-Balder Havenith, 2022. "An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping," Land, MDPI, vol. 11(8), pages 1-20, August.
    5. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
    6. Weizhang Liang & Suizhi Luo & Guoyan Zhao & Hao Wu, 2020. "Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    7. Fabio Luino & Mariano Barriendos & Fabrizio Terenzio Gizzi & Ruediger Glaser & Christoph Gruetzner & Walter Palmieri & Sabina Porfido & Heather Sangster & Laura Turconi, 2023. "Historical Data for Natural Hazard Risk Mitigation and Land Use Planning," Land, MDPI, vol. 12(9), pages 1-21, September.
    8. Fan Yang & Xiaozhi Men & Yangsheng Liu & Huigeng Mao & Yingnan Wang & Li Wang & Xiran Zhou & Chong Niu & Xiao Xie, 2023. "Estimation of Landslide and Mudslide Susceptibility with Multi-Modal Remote Sensing Data and Semantics: The Case of Yunnan Mountain Area," Land, MDPI, vol. 12(10), pages 1-15, October.
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