Spatiotemporal LSA modeling incorporating comprehensively the momentary effects of rainfall and earthquake: A case study of the Liangshan Prefecture, China

JY Li and WD Wang and GQ Chen and Z Han and CZ Zhu and C Chen, ADVANCES IN SPACE RESEARCH, 76, 6725-6740 (2025).

DOI: 10.1016/j.asr.2025.09.031

Landslides are one of the most destructive geo-hazards, and the landslide susceptibility assessment (LSA) can effectively reduce landslide risks and strengthen landslide prevention. The present study explores a spatiotemporal LSA method considering comprehensively the momentary effects of rainfall and earthquakes. Logistic regression model, random forest model, deep belief network (DBN) model, and grey wolf optimizer (GWO)-DBN model were used to analyze the spatial LSA, and the optimal spatial LSA obtained using the GWO-DBN model was chosen using various evaluation metrics to analyze the spatiotemporal LSA. Meanwhile, the historical landslide data during the year before the study time, namely from July 5, 2020 to July 5, 2021, and the data of rainfall and earthquake before various landslides were collected, and their effective rainfall and seismic peak ground acceleration were calculated to construct the temporal LSA regression model. The temporal LSA map in the study time was thus obtained and coupled with the optimal spatial LSA map to generate the spatiotemporal LSA map. Due to dynamic changes over time of spatiotemporal LSA, the precise landslide locations and ranges were obtained using small baseline subset interferometric synthetic aperture radar, and the results were coupled with spatiotemporal LSA map. There were 86.92% landslide regions with very high and high susceptibility, and the accuracy of spatiotemporal LSA was verified, which provides a reference for the spatiotemporal LSA verification method. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Return to Publications page