Application of Bayesian Approach to Dynamic Assessment of Flood in Urban Underground Spaces

Abstract

Urban underground facilities tend to be vulnerable to flood that is generated by the breaking of a dam or a levee, or a flash flood after an exceptional rainfall. Rapid and dynamic assessment of underground flood evolution process is of great significance for safety evacuation and disaster reduction. Taking advantage of the Delphi method to determine the Bayesian conditional probabilities collected by expert knowledge, this paper proposes an integrated Bayesian Network (BN) framework for rapidly and dynamically assessing the flood evolution process and consequences in underground spaces. The proposed BN framework, including seventeen nodes, can represent the flood disaster drivers, flood disaster bearers, flood mitigation actions, and on-site feedback information. Given evidences to specific nodes, the risk distribution of typical flood scenarios can be quantitatively estimated. The results indicate that the proposed framework can be useful for dynamically evaluating underground flood evolution process and identifying the critical influencing factors. This BN-based framework is helpful for “Scenario-Response”-based predictive analyses to support decision that is related to flood disaster emergency response.

Keywords

• flood
• underground spaces
• Bayesian Network
• emergency decision-making
• dynamic risk assessment