Urban flood inundation probability assessment based on an improved Bayesian model

Huang, Jing, Zhuo, Lu, She, Jingwen, Kang, Jinle, Liu, Zhenzhen and Wang, Huimin 2023. Urban flood inundation probability assessment based on an improved Bayesian model. Natural Hazards Review 24 (4) , 04023046. 10.1061/NHREFO.NHENG-1726

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Abstract

Urban flood inundation is spatially uncertain. To quantify this uncertainty, it is necessary to explore the spatial probability of urban flood inundation in different return periods. In this study, an urban flood spatial inundation probability assessment method based on an improved Bayesian model is proposed, which comprises three parts: data reconstruction based on undersampling; optimal Bayesian sample planning; and spatial inundation probability assessment. A case study of the central urban area of Jingdezhen City, China, is presented in this paper. The results indicate that (1) the inundation probabilities generated based on various return periods (20-, 50-, and 100-year return periods) are accurately determined and can provide more detailed inundation information. (2) The adoption of the random undersampling data reconstruction method solves the problem of an imbalanced number of inundations/noninundations during Bayesian modeling and substantially enhances the prediction accuracy compared with the traditional Bayesian modeling approach. (3) A sensitivity analysis reveals that inundation probability is sensitive to the drainage network and elevation rather than soil water retention and distance to river. With an increase in the return period, the inundation probability gradually increases. As the proposed method can quantify flood inundation uncertainty, it is valuable in supporting specific flood risk assessments.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Earth and Environmental Sciences
Publisher:	American Society of Civil Engineers
ISSN:	1527-6988
Date of First Compliant Deposit:	5 October 2023
Date of Acceptance:	2 March 2023
Last Modified:	11 Oct 2023 02:37
URI:	https://orca.cardiff.ac.uk/id/eprint/163004

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