Traditional landslide susceptibility methods often ignore the dynamic characteristics of influencing factors and the spatial dependencies between terrain units.To address this issue,a generalized additive model combining spatiotempo-ral relationships is proposed for spatiotemporal modeling of rainfall-induced shallow landslide susceptibility in southern Jiangxi Province.This method predicts the probability of landslide occurrence within specific terrain units over a given timeframe.Initially,Pearson correlation coefficients and a sequential forward feature selection method based on the Akaike Information Criterion(AIC)are employed to evaluate and select impact factors of landslide susceptibility.Subse-quently,a Bernoulli generalized additive spatiotemporal model is constructed by integrating spatiotemporal relationships to conduct dynamic susceptibility predictions.Finally,the spatiotemporal cross-validation method is used to assess the model's spatiotemporal predictive performance.The results indicate that the model demonstrates excellent fitting and pre-dictive capabilities.The spatiotemporal random cross-validation results show that the model's predictive performance re-mains stable across different percentages of training data,with an average prediction accuracy of 0.881.
rainfall-induced shallow landslideslandslide susceptibilityspatiotemporal modelinggeneralized additive modelspatiotemporal random cross-validation
万方数据
维普
