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A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence

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  • Haoran Fang

    (Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Laboratory of Target Microwave Properties, Deqing Academy of Satellite Applications, Huzhou 313200, China)

  • Yun Shao

    (Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Laboratory of Target Microwave Properties, Deqing Academy of Satellite Applications, Huzhou 313200, China)

  • Chou Xie

    (Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Laboratory of Target Microwave Properties, Deqing Academy of Satellite Applications, Huzhou 313200, China)

  • Bangsen Tian

    (Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China)

  • Chaoyong Shen

    (The Third Surveying and Mapping Institute of Guizhou Province, Guiyang 550004, China)

  • Yu Zhu

    (Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China)

  • Yihong Guo

    (Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China)

  • Ying Yang

    (Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Guanwen Chen

    (The Third Surveying and Mapping Institute of Guizhou Province, Guiyang 550004, China)

  • Ming Zhang

    (Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Landslides are a common and costly geological hazard, with regular occurrences leading to significant damage and losses. To effectively manage land use and reduce the risk of landslides, it is crucial to conduct susceptibility assessments. To date, many machine-learning methods have been applied to the landslide susceptibility map (LSM). However, as a risk prediction, landslide susceptibility without good interpretability would be a risky approach to apply these methods to real life. This study aimed to assess the LSM in the region of Nayong in Guizhou, China, and conduct a comprehensive assessment and evaluation of landslide susceptibility maps utilizing an explainable artificial intelligence. This study incorporates remote sensing data, field surveys, geographic information system techniques, and interpretable machine-learning techniques to analyze the sensitivity to landslides and to contrast it with other conventional models. As an interpretable machine-learning method, generalized additive models with structured interactions (GAMI-net) could be used to understand how LSM models make decisions. The results showed that the GAMI-net model was valid and had an area under curve (AUC) value of 0.91 on the receiver operating characteristic (ROC) curve, which is better than the values of 0.85 and 0.81 for the random forest and SVM models, respectively. The coal mining, rock desertification, and rainfall greater than 1300 mm were more susceptible to landslides in the study area. Additionally, the pairwise interaction factors, such as rainfall and mining, lithology and rainfall, and rainfall and elevation, also increased the landslide susceptibility. The results showed that interpretable models could accurately predict landslide susceptibility and reveal the causes of landslide occurrence. The GAMI-net-based model exhibited good predictive capability and significantly increased model interpretability to inform landslide management and decision making, which suggests its great potential for application in LSM.

Suggested Citation

  • Haoran Fang & Yun Shao & Chou Xie & Bangsen Tian & Chaoyong Shen & Yu Zhu & Yihong Guo & Ying Yang & Guanwen Chen & Ming Zhang, 2023. "A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3094-:d:1062011
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    References listed on IDEAS

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    1. Yongwei Li & Xianmin Wang & Hang Mao, 2020. "Influence of human activity on landslide susceptibility development in the Three Gorges area," 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. 104(3), pages 2115-2151, December.
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    Cited by:

    1. Li Zhuo & Yupu Huang & Jing Zheng & Jingjing Cao & Donghu Guo, 2023. "Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance," Sustainability, MDPI, vol. 15(11), pages 1-23, June.
    2. He Yang & Qihong Wu & Jianhui Dong & Feihong Xie & Qixue Zhang, 2023. "Landslide Risk Mapping Using the Weight-of-Evidence Method in the Datong Mining Area, Qinghai Province," Sustainability, MDPI, vol. 15(14), pages 1-27, July.

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