Assessing urban areas vulnerability to pluvial flooding using GIS applications and Bayesian Belief Network model

Abstract

Expected increases in intensity and frequency of rainfall extremes due to climate change, and increased paving and loss of water storage space in urban areas is making cities more susceptible to pluvial flooding. Evaluating the flood vulnerability of urban areas is a crucial step towards risk mitigation and adaptation planning. In this study, a coupled Geographic Information System and Bayesian Belief Network based flood vulnerability assessment model is proposed. The methodology can quantify uncertainty and capture the casual nexus between pluvial flood influencing factors. The model is applied in a case study to diagnose the reason behind the variations in the number of reported basement flooding in different parts of the City of Toronto and to predict Flood Vulnerability Index (FVI). The predicted FVI is validated by comparing the results with the number of approved basement flood subsidy protection program applications. The case study result shows that areas located near downtown Toronto have high FVI and most of the city has medium to low FVI.