Abstract
Rainfall-induced landslides often result in significant human and property losses, and reliable rainfall thresholds can effectively mitigate the hazards associated with them. However, constructing reliable rainfall thresholds in mountainous areas with sparse rain gauge stations is challenging. This study aims to establish reliable empirical rainfall thresholds for the landslide early warning systems (LEWSs) in the study area, utilizing radar-derived rainfall data processed by deep learning. Firstly, the accuracy of radar-derived rainfall data was verified based on the data with rain gauge measurements. Subsequently, utilizing frequency theory and Bayesian probability analysis methods, in conjunction with the collected landslide data and radar-derived rainfall data, various exceedance probability thresholds for rainfall-induced landslides were determined. Furthermore, the influence of cumulative effective antecedent rainfall on the initiation of landslides was investigated. The proposed threshold equations and the effect of antecedent rainfall on landslides are intended to aid in enhancing the LEWSs for this region. The findings provide valuable insights for managing rainfall-induced landslides, and can be applied to other areas with sparse rainfall data, offering a scientific basis for improved landslide prediction and risk management.