Modeling the Dynamics of Community Resilience to Coastal Hazards Using a Bayesian Network

Abstract

Studies on how variables of community resilience to natural hazards interact as a system that affects the final resilience (i.e., their dynamical linkages) have rarely been conducted. Bayesian network (BN), which represents the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distributions, offers an effective way to investigate the interactions among different resilience components and addresses the natural–human system as a whole. This article employs a BN to study the interdependencies of ten resilience variables and population change in the Lower Mississippi River Basin (LMRB) at the census block group scale. A genetic algorithm was used to identify an optimal BN where population change, a cumulative resilience indicator, was the target variable. The genetic algorithm yielded an optimized BN model with a cross-validation accuracy of 67 percent over a period of 906 generations. Six variables were found to have direct impacts on population change, including level of threat from coastal hazards, hazard damage, distance to coastline, employment rate, percentage of housing units built before 1970, and percentage of households with a female householder. The remaining four variables were indirect variables, including percentage agriculture land, percentage flood zone area, percentage owner-occupied house units, and population density. Each variable has a conditional probability table so that its impacts on the probability of population change can be evaluated as it propagates through the network. These probabilities could be used for scenario modeling to help inform policies to reduce vulnerability and enhance disaster resilience.