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2024 Vol. 44, No. 3
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DAI Yong, MENG Qingkai, CHEN Shilong, LI Wei, YANG Liqiang. 2024. Landslide hazard evaluation based on BPNN-SHAP model: A case study of the Yili River Basin, Xinjiang Province. Sedimentary Geology and Tethyan Geology, 44(3): 534-546. doi: 10.19826/j.cnki.1009-3850.2024.07006
Citation: DAI Yong, MENG Qingkai, CHEN Shilong, LI Wei, YANG Liqiang. 2024. Landslide hazard evaluation based on BPNN-SHAP model: A case study of the Yili River Basin, Xinjiang Province. Sedimentary Geology and Tethyan Geology, 44(3): 534-546. doi: 10.19826/j.cnki.1009-3850.2024.07006
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Landslide hazard evaluation based on BPNN-SHAP model: A case study of the Yili River Basin, Xinjiang Province

  • 1.

    School of Civil Engineering and Water Resources, Qinghai University, Xining 810016, China

  • 2.

    State Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China

  • 3.

    College of Geophysics, Chengdu University of Technology, Chengdu 610059, China

  • 4.

    College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China

  • 5.

    The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China

More Information
Received Date:  30 May 2024
Revised Date:  01 July 2024
Accepted Date:  04 July 2024
Publish Date:  30 September 2024
    Abstract

  • To further improve the accuracy of landslide hazard prediction models and enhance their interpretability, this study selected 8 influencing factors of landslide occurrence, taking the Yili River Basin, Xinjiang province as an example. An interpretable BPNN-SHAP model, based on the back propagation neural network (BPNN) model and the game theory with the aim of addressing the 'black box' issue, was constructed. Firstly, the dataset was divided into 70% training set and 30% test set, and 5-fold cross-validation was used to enhance the robustness of the BPNN-SHAP model. Then, the evaluation accuracy of this model was compared with three other models: Deep Neural Network (DNN), Random Forest (RF), and Logistic Regression (LR). Finally, regional landslide hazard assessment was completed, and the interpretability of BPNN-SHAP was also discussed. The results showed that the BPNN-SHAP model achieved the highest statistical values in the following metrics: Accuracy (A)=0.904, Precision (P)=0.911, Recall (R)=0.919, F1Score=0.915, and SAUC=0.905. The very high and high danger areas for landslides in the study region accounted for 11.96% and 15.53%, respectively. Among these regions, Xinyuan and Nileke County occupy the highest proportions, at approximately 51.1% and 45.6%, respectively. The primary controlling factors for landslides were elevation, slope, rainfall, and peak ground acceleration (PGA). Specifically, areas with an elevation of 1500 m to 2000 m, slopes greater than 14°, annual rainfall between 260 mm and 310 mm, and PGA greater than 0.23 g are prone to landslides, indicating that the predominant types of landslides are rainfall-induced and earthquake-induced. Our research method is expected to provide a new technical reference for landslide hazard assessment and theoretical support for disaster prevention, mitigation, and resilience construction in the Yili River Basin.

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