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Modeling of Inter-Organizational Coordination Dynamics in Resilience Planning: A Multilayer Network Simulation FrameworkThis paper proposed and tested a multilayer framework for modeling network dynamics of inter-organizational coordination in resilience planning among interdependent infrastructure sectors. Each layer in the network represents one infrastructure sector such as flood control, transportation, and emergency response. Coordination probability was introduced to approximate the inconsistent coordination between organizations, based on which the intra-layer or inter-layer link removal was conducted and inter-organizational coordination efficiency within and across infrastructure sectors was hereby unveiled. To test the proposed framework, a multilayer collaboration network of 35 organizations from five infrastructure sectors in Harris County, Texas, was mapped based on a survey of Hurricane Harvey. The analysis results showed that before Hurricane Harvey, coordination among flood control, transportation, and infrastructure development sectors lacked essential integration to foster robust resilience plans. The proposed framework enables an assessment of coordination efficiency among organizations involving in resilience planning and provides an indicator for urban resilience measurement.
Characterization of the Vulnerability of Road Networks to Fluvial Flooding Using Network Percolation ApproachThe 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.