Flood Disaster Rapid Simulation and Risk Assessment Technology Based on Spatiotemporal Data
With global warming, flood disasters caused by rainstorms have been occurring frequently in recent years.Therefore, it is necessary to carry out disaster loss and risk assessment.How to quickly and accurately achieve disaster risk assessment is a major problem in risk assessment work.In this paper, we constructed a disaster risk assessment and disaster area risk prediction analysis model from the two aspects of loss degree and danger in disaster risk assessment.First, we realized the rapid simulation of inundation area, inundation depth, and flow velocity by using the inundation simulation and estimation model of flood disaster.Second, on the basis of simulation results of the flooding model, combined with multisource spatiotemporal data, we constructed a dynamic assessment model for disaster losses and a bridge hazard analysis model to effectively evaluate the spatial distribution of damaged buildings, construction land, agricultural land, and bridges in a disaster area.Third, in this paper, we propose a disaster risk prediction and analysis technique to predict the spatial distribution of areas potentially affected by rainfall.Finally, we selected three regions in Beijing as experimental areas, namely, Mentougou District, Fangshan District, and Changping District, where an extremely heavy rainstorm occurred in July 2023.On the basis of the above models, the risk assessment and prediction analysis of rainstorm disasters were carried out, which proved that the models proposed can provide data support for rapid disaster assessment and leadership decision-making to a certain extent.
Authors
- Yusheng Zhou (ORCID: https://orcid.org/0000-0002-8795-3423)
- Yanyan Zeng (ORCID: https://orcid.org/0000-0002-6560-3043)
- Zongxia Xu
- Yingchun Tao
- Kui Zhang (ORCID: https://orcid.org/0000-0002-8370-8412)
- Xuping Zhang (ORCID: https://orcid.org/0000-0001-6826-5599)
- H. Liang (ORCID: https://orcid.org/0009-0000-9528-1260)
Institutions
- Chinese Academy of Surveying and Mapping (CN)
Publication Details
- Journal
- Sensors and Materials
- Published
- 2025-04-18
- DOI
- https://doi.org/10.18494/sam5295
- Primary Topic
- Advanced Computational Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00