Improving hurricane-related flood risk assessments in urban areas: Integrating a Bayesian network modelling approach with an indicator-based approach

Feng, Dianyu (2024) Improving hurricane-related flood risk assessments in urban areas: Integrating a Bayesian network modelling approach with an indicator-based approach. PhD thesis, University of Glasgow.

Abstract

Globally, hurricane-induced flooding poses a significant threat, particularly for urban areas with typically high populations and infrastructure densities. Comprehensive flood risk assessment is becoming increasingly important for understanding the potential impacts of hurricane-induced flooding on an urban area. In this context, this research contributes to developing urban flood risk assessments using an indicator-based approach and Bayesian network modelling approaches, comprising three incremental steps.

The first step is based on an indicator-based flood risk assessment. Through a literature review, this research found that most assessments often overlook urban ecosystem elements (accounting for less than 15% of total indicators), focusing more on social and economic aspects. To address this gap, this research proposed a social-ecological systems (SES) urban flood risk assessment framework with 117 indicators. Flood risks were then analysed in Houston during Hurricane Harvey, applying the improved analytic hierarchy process (IAHP) and equal weighting methods. The results showed that the equal weighting method identified a wider range of high flood risk areas, with both methods indicating that Houston’s western parts were at the highest flood risks during Hurricane Harvey.

The second step aims to enhance the proposed framework with a Bayesian network (BN) model. This innovative approach can overcome shortcomings of the indicator-based method, such as its inability to incorporate new information and quantify uncertainties. The results showed that Houston’s western and north-eastern regions faced the highest flood risks during Hurricane Harvey. Overall, the BN model’s performance was largely in line with an indicator-based approach, with over 80% similarity in outcomes.

To further enhance the BN model, the third step was to develop a dynamic BN (DBN) model to obtain a more detailed understanding of urban flood risks. In this model, two hazard nodes were specifically designated as dynamic, while exposure and vulnerability indicators were treated as static. The comparison of results from the BN model and the DBN model shows that the latter efficiently captures the dynamic nature of flood risks.

Overall, the research offers a perspective on the enhancement of the indicator-based approach through the BN modelling approach. This research can help in identifying more effective disaster risk management strategies, offering insights into the evolving nature of flood risks in urban settings.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
• Colleges/Schools: College of Social Sciences > School of Social & Environmental Sustainability
• Supervisor's Name: Renaud, Professor Fabrice and Shi, Dr. John Xiaogang
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84573
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 18 Sep 2024 14:57
• Last Modified: 23 Sep 2024 12:02
• Thesis DOI: 10.5525/gla.thesis.84573
• URI: https://theses.gla.ac.uk/id/eprint/84573