Doctor of Philosophy (PhD)
This dissertation presents the assessment and modeling of community resilience to coastal hazards in the Lower Mississippi River Basin (LMRB) in southeastern Louisiana at the census block group scale. The first part is to provide a quantitative method to assess and validate the community resilience index to coastal hazards and identify the relationships between a set of socio-environmental indicators and community resilience. The Resilience Inference Measurement (RIM) model was applied to assess the resilience of the block groups. The resilience index derived was empirically validated through two statistical procedures: K-means cluster analysis of three variables (exposure, damage, and recovery) to derive the resilience groups and discriminant analysis to identify the key indicators of resilience. The discriminant analysis yielded a classification accuracy of 73.1%. Block groups with higher resilience were concentrated in the northern part, whereas lower-resilience communities were located mostly along the coastline and lower-elevation area. The second part investigates the interactions among different resilience components and addresses the natural-human system as a whole. A Bayesian Network (BN) was employed to represent the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distribution. A genetic algorithm was used to identify an optimal BN, where population change was used as the target variable to indicate long term recovery and resilience outcome. The genetic algorithm yielded an optimized BN structure with a cross-validation accuracy of 66.9% over a period of 906 generations. Six variables were found to have direct impacts on population change, including hazard exposure, hazard damages, distance to coastline, employment rate, percent of housing units built before 1970, and percent of households with female householder. The remaining four variables are indirect variables, including percent agriculture land, percent flood zone area, percent owner-occupied house units, and population density. In the third part, simulation analysis of population changes under five different scenarios was conducted using the optimized BN model. Results indicate by reducing vulnerability or improving resilience capacity, most block groups would experience stable population growth, however, block groups in the New Orleans area and along the coastline area always have population decrease under all scenarios.
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Cai, Heng, "Assessing and Modeling Community Resilience to Coastal Hazards Using a Bayesian Network" (2017). LSU Doctoral Dissertations. 4369.
Available for download on Friday, July 12, 2024