Explainable Susceptibility Modelling of Urban Ground Collapse Considering Dynamic Rainfall and Background Controls: A Case Study in Shenzhen, China

Urban ground collapse (UGC) threatens dense coastal cities because failures can occur abruptly beneath roads and buried lifelines. We developed a dynamic and interpretable machine-learning framework for UGC susceptibility assessment in Shenzhen, China. We integrated 1687 events from 2017 to 2024 with 20 predictors covering terrain, rainfall, drainage infrastructure and urban disturbance. Event-date rainfall was assigned to collapse samples, and background controls received year-constrained pseudo-event dates after spatial exclusion. Three tree-ensemble models were evaluated under random 75/25 testing, leave-one-district-out spatial validation and temporal hold-out testing. The selected LightGBM 1:5 model achieved ROC-AUC = 0.934, AP = 0.790 and BA = 0.860 in random testing, with more conservative ROC-AUC values of 0.905 and 0.884 under spatial and temporal validation. High-rainfall mapping expanded high and very high susceptibility zones from 4.60% to 15.26% and increased event capture from 19.32% to 58.92%. Grouped SHAP indicated comparable pipeline and rainfall contributions (26.01% and 25.13%). The leading predictors were pipe burial depth, road density and 30-day rainfall. PDP and spatial SHAP diagnostics highlighted high model responses where deep pipes coincided with high antecedent rainfall or dense roads in mature urban cores. This framework supports the rainfall-conditioned, mechanism-informed susceptibility diagnosis for early warning and mitigation.