Incorporating mitigation strategies in machine learning for landslide susceptibility prediction

Abstract

This study proposes an approach that considers mitigation strategies in predicting landslide susceptibility through machine learning (ML) and geographic information system (GIS) techniques. ML models, such as random forest (RF), logistic regression (LR), and support vector classification (SVC) are incorporated into GIS to predict landslide susceptibilities in Hong Kong. To consider the effect of mitigation strategies on landslide susceptibility, non-landslide samples were produced in the upgraded area and added to randomly created samples to serve as ML models in training datasets. Two scenarios were created to compare and demonstrate the efficiency of the proposed approach; Scenario I does not considering landslide control while Scenario II considers mitigation strategies for landslide control. The largest landslide susceptibilities are 0.967 (from RF), followed by 0.936 (from LR) and 0.902 (from SVC) in Scenario II; in Scenario I, they are 0.986 (from RF), 0.955 (from LR) and 0.947 (from SVC). This proves that the ML models considering mitigation strategies can decrease the current landslide susceptibilities. The comparison between the different ML models shows that RF performed better than LR and SVC, and provides the best prediction of the spatial distribution of landslide susceptibilities.

Keywords

Machine learning / Landslide susceptibility / Spatial prediction / Mitigation strategies

CRediT authorship contribution statement

Haoyue Zhang: Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft. Xujiao Zhang: Conceptualization, Funding acquisition, Investigation, Writing – review & editing. Peisheng Ye: Investigation, Resources. Chenglu Li: Data curation, Investigation. Junlei Li: Data curation, Investigation. Xiaoning Yuan: Data curation, Investigation. Xiangge Zhang: . Huaming Guo: Validation, Funding acquisition. Pat J.-F. Yeh: Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 42301094, 41972192, and 41825017). Prof. Dicheng Zhu gave considerable assistance with the zircon U-Pb dating work. Dr. Barbara Rumsby and Prof. Paul A Carling were involved in the professional polishing work. Dr. Di Zhang gave help with arsenic analysis. The editor and anonymous reviewers are also thanked for their careful reviews.

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