Files in this item

FilesDescriptionFormat

application/pdf

application/pdfBOAKYE-DISSERTATION-2020.pdf (5MB)
(no description provided)PDF

Description

Title:Modeling the societal impact of natural hazards
Author(s):Boakye, Jessica S
Director of Research:Gardoni, Paolo
Doctoral Committee Chair(s):Gardoni, Paolo
Doctoral Committee Member(s):Murphy, Colleen; Chang, Kevin; Liang, Feng
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Natural Hazards
Societal Impacts
Capability Approach
Data Analytics
Abstract:This dissertation will present models for societal consequences of natural hazards which are often neglected in traditional risk analysis. The impact of natural hazards is far-reaching and evident based on past disasters such as Hurricane Katrina (2005) and Superstorm Sandy (2012). The vulnerability of the nation’s infrastructure can lead to significant physical damage which can translate into significant societal suffering. This societal suffering often outweighs the physical damage to infrastructure. However, traditional definitions of consequences are narrow and often limited to monetary loss. Since natural hazards are low frequency, high impact events, decision makers are reliant on engineering models to simulate disaster impacts. To mitigate these impacts, realistic models must be developed which can properly translate physical damage to infrastructure systems into societal consequences. Additionally, insufficient or missing data has traditionally forced researchers to develop simple models often applied to overly simplistic scenarios. The growth in data sharing and model complexity provides researchers with an opportunity to improve and expand existing models. This dissertation will integrate data analytics into predictive models to overcome data implications. The dissertation will also introduce metrics for the evaluation of societal consequences with particular focus towards the concepts of community resilience and sustainability. The dissertation will first introduce a Capability Approach (CA) for conceptualizing and quantifying societal impacts. Capabilities are the genuine opportunities that individuals have to achieve valuable doings and beings. Additionally, the dissertation discusses the CA from the perspective of groups since the CA is traditionally defined from the perspective of the individual. Groups are especially salient in the context of natural hazards since vulnerable groups may experience disproportionate hazard impacts. The creation of community resilience and sustainability goals to promote hazard mitigation is also discussed. Quantification metrics are presented using the example of the failure of transportation infrastructure in a real community subject to a hypothetical earthquake hazard. Technological advances and the growth of data sharing has influenced research in a multitude of applications and researchers have been presented with an opportunity to improve, enhance, and expand existing research fields. The emergence of big data, or data which is large in size or complexity, has led to new challenges as well. These data, although useful, are difficult to analyze and often cannot be handled using traditional systems. New algorithms are constantly developed to try to manage these data systems. In the context of hazard management, where we are often dealing with low frequency events, data is usually a limiting factor for research. This dissertation discusses the opportunities and considerations for integrating these technological advances and new algorithms into risk analysis for natural hazards. As an example, data analytics are used to construct a dataset and prediction model for household income which is important for hazard modeling.
Issue Date:2020-12-04
Type:Thesis
URI:http://hdl.handle.net/2142/109435
Rights Information:Copyright 2020 Jessica Boakye
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12


This item appears in the following Collection(s)

Item Statistics