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Abstract
The assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively.
Acknowledgements
We the authors thank the editors and anonymous reviewers for their comments and suggestions, which are of great help to the writing of this article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The 12.5 m spatial resolution DEM used in this study was downloaded from https://search.asf.alaska.edu/#/; Rainfall precipitation is downloaded from https://www.resdc.cn/DOI/.