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

Haoran Fang, Yun Shao, Chou Xie (), Bangsen Tian, Chaoyong Shen, Yu Zhu, Yihong Guo, Ying Yang, Guanwen Chen and Ming Zhang
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Haoran Fang: Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China
Yun Shao: Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China
Chou Xie: Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, 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
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

Sustainability, 2023, vol. 15, issue 4, 1-22

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.

Keywords: landslides susceptibility map; explainable AI; GIS; Karst landform; coal mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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