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. Graphical abstract: [Figure not available: see fulltext.]