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Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment
Author(s) -
Dong Shangjia,
Yu Tianbo,
Farahmand Hamed,
Mostafavi Ali
Publication year - 2020
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12527
Subject(s) - flooding (psychology) , flood myth , cascade , cascading failure , flood control , computer science , vulnerability (computing) , bayesian network , resilience (materials science) , vulnerability assessment , component (thermodynamics) , data mining , environmental science , psychological resilience , engineering , geography , artificial intelligence , computer security , thermodynamics , psychology , power (physics) , physics , electric power system , archaeology , quantum mechanics , chemical engineering , psychotherapist
Abstract This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision‐makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near‐real‐time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas.