An Integrated GIS-BBN Approach to Quantify Resilience of Roadways Network Infrastructure System against Flood Hazard

Abstract

Infrastructure resilience is defined as the ability of a system to withstand and recover from the effects of natural or man-made hazards. For any community, quantifying its sociophysical infrastructure resilience during and after any disruptive event is important for planners, designers, and decision-makers. However, a global approach for resilience quantification becomes challenging due to the fact that infrastructure systems’ performance varies from location to location and the recovery process is also complex and region-specific. In this work, an integrated Geographic Information System (GIS)-Bayesian Belief Network (BBN) framework is developed to model and quantify the resilience (vulnerability and recovery) of network infrastructure systems against flood hazards. To this end, a simple case study is demonstrated for quantifying flood resilience of a roadway network in a community in northeast India. Data collection is done using a GIS platform and a probabilistic graphical model (BBN model) is used to model uncertainties in resilience quantification based on the available data and judgments. The main contributions of the proposed resilience model are: (1) the model can provide more accurate and realistic estimates based on beliefs; (2) the model can be updated as and when more data is available; and (3) sensitivity analysis of the validated road network resilience model to facilitate risk-informed decision-making against future flood disaster.