Urban flood disaster risk evaluation based on ontology and Bayesian Network

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 vulnerable to pluvial flooding. It is a crucial step towards risk mitigation and adaptation planning to evaluate and predict flood disaster risk. In this study, a model coupling ontology and Bayesian Network that can capture the potential relationships between different factors influencing flood disaster and has capable of quantifying uncertainty and utilizing both data and observations was proposed. Based on the proposed model, the flood disaster risk in Zhengzhou City was assessed, which was validated by comparing with actual historical flood records. And the sensitivity analysis was carried out based on this model to determine that which factors would affect the disaster most. The results show that 72.3% of the study area was characterized by very-high to moderate flood disaster risk, which was mainly located in the central and eastern regions. The sensitivity analysis show that the rainfall duration is the most impactful factor, and the factors related to disaster-formative environment have low effect. The results revealed quite good agreement between the predicted flood disaster risk with actual flood records, which can be useful to assist flood mitigation and management. The model proposed in this study offers the Zhengzhou City and flood researches around the world, an effective way for evaluating flood disaster risk to manage uncertainty in water availability under climate change.