Regional debris flow risk dynamic assessment method based on space-time probability
Through a method based on space-time probability, the risk of mudslides is dynamically evaluated, which solves the problems of model limitations and insufficient data utilization in traditional methods, and realizes accurate dynamic assessment of mudslideslides hazards and effective assessment and decision-making support for complex natural disasters.
Patent Information
- Application Number
- CN202510192467.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-21
- Publication Date
- 2025-03-25
- Estimated Expiration
- 2045-02-21
AI Technical Summary
Traditional mudslide risk assessment methods have model limitations, insufficient data utilization and lack of dynamic analysis capabilities, and it is difficult to fully reflect the complex trigger mechanism of mudslides and the nonlinear interaction of environmental variables, and fail to make full use of multidimensional information and reflect the impact of climate change on mudslide risks.
The dynamic assessment method of regional mudslide hazards based on spatiotemporal probability is adopted. By collecting basin unit data and grid data, the relationship between historical mudslide data and meteorological data is analyzed, the meteorological condition threshold is determined, the meteorological condition frequency and future time probability are calculated, the spatial probability is calculated based on the substance source distribution and basin connectivity index, and the mudslide susceptibility value is constructed through the random forest model, and the comprehensive hazard index is finally calculated through information entropy.
Accurate dynamic assessment of the risk of mudslides is achieved, and the result deviation caused by ignoring the dynamic characteristics of time and insufficient integration of multi-source data in traditional methods is solved, the accuracy and applicability of the model is improved, and the evaluation and decision-making support capabilities for complex natural disasters are enhanced.