Integrating Data into Community Resilience: A Bayesian Approach to Assessing Flood Resilience in Built Environments

In the face of escalating environmental challenges, fostering resilient communities requires innovative approaches to understanding and mitigating the impacts of natural disasters. This research introduces a novel Bayesian network model specifically designed to quantify flood resilience within built environments. This model integrates empirical data and expert insights to assess the influence of various structural and environmental factors on the capacity of communities to withstand and rapidly recover from flood events. The study commenced with an extensive survey aimed at collecting critical resilience-related data variables, broadly categorized into building-specific and flood-specific groups. The Manchester and Cumbria, recognized as high-level flood risk regions, were selected for data collection across twelve community zones with varying risk-levels. Through the application of Principal Component Analysis and Pearson Correlation Analysis, significant resilience factors were identified, and dynamic patterns and interrelationships were determined, laying the foundation for model development. By categorizing the influential key variables into actionable states of risk and analyzing their influence on key resilience parameters at gradated resilience-levels, the model quantified the resilience levels in Manchester and Cumbria offering a granular perspective on flood resilience. The model dynamically refines its predictions, enhancing its relevance and applicability to real-world flood scenarios. It further signifies the interconnectedness of built asset resilience and community well-being, highlighting the role of informed decision-making in strengthening societal resilience against floods. Utilizing actual datasets, the Bayesian model not only sheds light on the determinants of structural resilience but also serves as a crucial decision-support tool for urban planners, policymakers, and community leaders.