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Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

Author

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  • Deliang Sun

    (Key Laboratory of GIS Application Research, School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China)

  • Danlu Chen

    (Key Laboratory of GIS Application Research, School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China)

  • Jialan Zhang

    (National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Changlin Mi

    (Linyi Natural Resources Development Service Center, Linyi 276000, China)

  • Qingyu Gu

    (Key Laboratory of GIS Application Research, School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China)

  • Haijia Wen

    (National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Key Laboratory of New Technology for Construction of Cities in Mountain Area, School of Civil Engineering, Chongqing University, Chongqing 400045, China)

Abstract

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as the corrosion layered high and middle mountain region (Zone I), and three counties (Wulong, Pengshui and Shizhu counties) in southeastern Chongqing, delineated as the middle mountainous region of strong karst gorges (Zone II), as the study area. This study used a Bayesian optimization algorithm to optimize the parameters of the LightGBM and XGBoost models and construct evaluation models for each of the two regions. The model with high accuracy was selected according to the accuracy of the evaluation indicators in order to establish the landslide susceptibility mapping. The SHAP algorithm was then used to explore the landslide formation mechanisms of different landforms from both a global and local perspective. (3) Results: The AUC values for the test set in the LightGBM mode for Zones I and II are 0.8525 and 0.8859, respectively, and those for the test set in the XGBoost model are 0.8214 and 0.8375, respectively. This shows that LightGBM has a high prediction accuracy with regard to both landforms. Under the two different landform types, the elevation, land use, incision depth, distance from road and the average annual rainfall were the common dominant factors contributing most to decision making at both sites; the distance from a fault and the distance from the river have different degrees of influence under different landform types. (4) Conclusions: the optimized LightGBM-SHAP model is suitable for the analysis of landslide susceptibility in two types of landscapes, namely the corrosion layered high and middle mountain region, and the middle mountainous region of strong karst gorges, and can be used to explore the internal decision-making mechanism of the model at both the global and local levels, which makes the landslide susceptibility prediction results more realistic and transparent. This is beneficial to the selection of a landslide susceptibility index system and the early prevention and control of landslide hazards, and can provide a reference for the prediction of potential landslide hazard-prone areas and interpretable machine learning research.

Suggested Citation

  • Deliang Sun & Danlu Chen & Jialan Zhang & Changlin Mi & Qingyu Gu & Haijia Wen, 2023. "Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation," Land, MDPI, vol. 12(5), pages 1-37, May.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:5:p:1018-:d:1140267
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    References listed on IDEAS

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    1. Jiakai Lu & Chao Ren & Weiting Yue & Ying Zhou & Xiaoqin Xue & Yuanyuan Liu & Cong Ding, 2023. "Investigation of Landslide Susceptibility Decision Mechanisms in Different Ensemble-Based Machine Learning Models with Various Types of Factor Data," Sustainability, MDPI, vol. 15(18), pages 1-49, September.

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