Quantitative Hazard Prediction of Rainfall-Induced Shallow Landslides Considering Triggering and Predisposing Factors: A Case of Natural Terrain Landslides in Hong Kong

In recent years, torrential rainfall has triggered numerous shallow landslides in southeastern China. Therefore, giving a spatial-temporal hazard prediction for rainfall-induced shallow landslides on a fine-grained scale is imperative. Nowadays, most empirical hazard prediction methods are qualitative and consider only rainfall features. The generated prediction results lack sufficient spatial details and have a high false alarm rate. By improving the traditional hazard prediction method based on frequency statistics and utilizing landslide susceptibility, this paper proposes a quantitative hazard prediction method that simultaneously considers landslide triggering and predisposing factors. In the case of predicting natural terrain landslide hazards in Hong Kong, the spatial accuracy and fineness of the prediction results generated by the proposed methods are significantly enhanced compared to those predicted by the qualitative method based on rainfall features, thereby confirming the efficacy, reliability, and benefits of the proposed methods.