Spatiotemporal landslide susceptibility assessment integrating typhoon tracks: a case study of typhoon Lekima

The 2019 Typhoon Lekima triggered extensive landslides in Zhejiang Province. To explore the impact of typhoon paths on the distribution of landslide susceptibility, this study proposes a spatiotemporal zoning assessment framework based on typhoon paths and inner rainbands. According to the typhoon landing path and its rainfall impact range, the study area is divided into the typhoon event period (TEP) and the annual non-typhoon period (ANP). The model uses 14 environmental factors, with the only difference between TEP and ANP being the rainfall index: TEP uses 48-hour rainfall during the typhoon, while ANP uses multi-year average annual rainfall. Modeling and comparative analysis were conducted using six machine learning models including random forest (RF) and support vector machine (SVM). The results show that the distribution pattern of high-risk landslide areas during TEP is significantly correlated with typhoon intensity: when the intensity is level 12, high-risk areas are radially distributed; at levels 10-11, they tend to concentrate asymmetrically along the coast; and when the intensity drops to below level 9, the overall susceptibility decreases significantly. During ANP, the distribution of landslides is relatively uniform with no obvious spatial concentration. Analysis on the factor contribution rate indicates that the rainfall weight in TEP is as high as 32.1%, making it the dominant factor; in ANP, the rainfall weight drops to 13.6% while the influence of factors such as slope and topographic wetness index increases, revealing differences in landslide formation mechanisms between the two periods. This study demonstrates that the spatiotemporal zoning method based on typhoon paths can effectively characterize the spatial susceptibility patterns of landslides and improve disaster identification capabilities under extreme weather conditions. The finally generated annual susceptibility zoning map divides the study area into four types of risk regions, providing a reference for dynamic monitoring and differentiated risk management of landslides in typhoon-prone areas.