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Title: Characterization of the Vulnerability of Road Networks to Fluvial Flooding Using Network Percolation Approach
The objective of this paper is to model and characterize the percolation dynamics in road networks during a major fluvial flooding event. First, a road system is modelled as planar graph, then, using the level of co-location interdependency with flood control infrastructure as a proxy to the flood vulnerability of the road networks, it estimated the extent of disruptions each neighborhood road network experienced during a flooding event. Second, percolation mechanism in the road network during the flood is captured by assigning different removal probabilities to nodes in road network according to a Bayesian rule. Finally, temporal changes in road network robustness were obtained for random and weighted-adjusted node-removal scenarios. The proposed method was applied to road flooding in a super neighborhood in Houston during hurricane Harvey. The result shows that, network percolation due to fluvial flooding, which is modelled with the proposed Bayes rule based node-removal scheme, causes the decrease in the road network connectivity at varying rate.
Award ID(s):
Publication Date:
Journal Name:
ASCE Computing in Civil Engineering 2019
Sponsoring Org:
National Science Foundation
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