Flood Hazard Analysis Based on Copula Connect Function
Publication: Natural Hazards Review
Volume 24, Issue 1
Abstract
The study adopts the Copula function to evaluate index data and historical flood disaster simulation samples to reduce the subjectivity of the evaluation results. A genetic algorithm is used to calculate the model parameters and predict flood hazard levels. The spatial data processing technology of the geographic information system (GIS) is employed to extract and analyze spatial data to acquire indicators. A comprehensive hazard evaluation index system containing a maximum of 1, 6, 24 h heavy rain, relative height difference, average gradient, and drainage density is established to perform detailed analysis. The complex links of the evaluation index values to flood hazard analysis are uncovered by applying this data-focused flood hazard evaluation strategy. By comparing the actual occurrence times and forecast results of flood disasters in 64 research areas of Hubei Province, we find the established model has good prediction effect and can provide data support for flood disaster early warning.
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Data Availability Statement
All data included in this study are available upon request from the corresponding author.
Acknowledgments
This work is supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN201903802), the Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0790), the State Key Program of National Natural Science of China of Major Research Projects (No. 91547208).
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Information & Authors
Information
Published In
Natural Hazards Review
Volume 24 • Issue 1 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jan 16, 2022
Accepted: Aug 4, 2022
Published online: Oct 31, 2022
Published in print: Feb 1, 2023
Discussion open until: Mar 31, 2023
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