Leveraging artificial intelligence to quantify slope rainfall sensitivity for refining regional landslide rainfall thresholds

Regional-scale landslide early warning systems are commonly developed based on empirical rainfall thresholds. However, current rainfall threshold models overlook the significant spatial variations in slopes response to rainfall, thus undermining the accuracy of warnings. In this study, we improved a regional-scale landslide early warning method based on rainfall thresholds by accounting for the varying rainfall sensitivity of individual slope units. First, the Rainfall Sensitivity Index (RSI) for both dry and wet seasons are computed using a Graph Attention Network (GAN) model, incorporating ten influencing factors. Subsequently, an initial regional critical rainfall threshold was obtained based on the Effective cumulative rainfall – rainfall Duration (E-D). Finally, the improved rainfall thresholds for each slope units are derived by coupling the initial critical rainfall threshold and RSI. Using Pingyang County, Zhejiang Province, China as test site, the reliability and reasonableness of the proposed method was validated by statistical analysis, in-situ tests and historical rainfall events. The results reveal that the GAN demonstrates robust predictive capability in RSI assessment, achieving a precision of 0.88. Notably, slopes exhibit higher rainfall sensitivity during dry season compared to wet season. The early warning system employing improved critical thresholds shows significant improvement, with 11.9 % higher accuracy and 14.3 % fewer missed alarms relative to conventional methods. Overall, this study proposes a novel method for downscaling of regional-scale thresholds to slope-unit levels in landslide early warning systems, which informs risk mitigation strategies and governmental decision-making, thereby effectively reducing the risk of landslides.