Degree

Doctor of Philosophy (PhD)

Department

Engineering Science

Document Type

Dissertation

Abstract

Assessing the costs and benefits of hazard mitigation efforts is an essential component of disaster management, planning, and resilience assessment. These calculations are particularly important in locations vulnerable to multiple hazards with high frequencies, such as coastal Louisiana. This study aims to provide an improved understanding of the costs and benefits of flood mitigation efforts in Louisiana funded by federal government grants between 2005 and 2015. Project data provided by the Governor’s Office of Homeland Security and Emergency Preparedness (GOHSEP) were summarized and missing values were imputed using robust statistical approaches. Elevation project cost was investigated for prediction by statistical modeling. Benefit analysis was then conducted by quantifying the avoided loss (AL) achieved through mitigation for discrete events and the benefit-cost ratio (BCR) was determined using a Monte Carlo simulation analysis. The data imputation study revealed that statistical methods can impute missing pre- and post-mitigation first floor elevation (FFE) values for use in AL analysis. The elevation mitigation project cost study was accomplished using multiple regression, random forest, and neural network models, with neural network demonstrating better prediction compared with other models. The mitigation project cost study revealed that, due to the accommodation of a wide range of building types, the statistical modeling of mitigation project cost is superior to the current mitigation cost guidelines. The AL and BCR analysis indicate that flood-mitigated residential buildings were associated with an average AL of $114,206 and BCR of 0.76 after two recent floods in Louisiana, respectively. Also, Monte Carlo simulation on probabilistic events revealed an average BCR of 1.69 for flood-mitigated projects in Jefferson Parish, Louisiana.

Date

12-29-2018

Committee Chair

Carol J. Friedland

Available for download on Sunday, December 29, 2019

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