干旱区生态环境知识资源中心(Arid): A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence

Arid

DOI	10.3390/su15043094
	A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence
	Fang, Haoran; Shao, Yun; Xie, Chou; Tian, Bangsen; Shen, Chaoyong; Zhu, Yu; Guo, Yihong; Yang, Ying; Chen, Guanwen; Zhang, Ming
通讯作者	Xie, C
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2023
卷号	15 期号:4
英文摘要	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.
英文关键词	landslides susceptibility map explainable AI GIS Karst landform coal mining
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000941471900001
WOS关键词	RANDOM FOREST ; ALGORITHMS ; REGRESSION ; TREE
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398765
推荐引用方式 GB/T 7714	Fang, Haoran,Shao, Yun,Xie, Chou,et al. A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence[J],2023,15(4).
APA	Fang, Haoran.,Shao, Yun.,Xie, Chou.,Tian, Bangsen.,Shen, Chaoyong.,...&Zhang, Ming.(2023).A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence.SUSTAINABILITY,15(4).
MLA	Fang, Haoran,et al."A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence".SUSTAINABILITY 15.4(2023).