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Title:Investigation of climate change impact on hurricane wind and freshwater flood risks using machine learning techniques
Author(s):Lin, Chi-Ying
Director of Research:Cha, Eun Jeong
Doctoral Committee Chair(s):Cha, Eun Jeong
Doctoral Committee Member(s):Gardoni, Paolo; Wang, Zhuo; Zhu, Ruoqing
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Artificial neural network
Climate change
Freshwater flooding
Residential buildings
Risk assessment
Abstract:Hurricane causes severe damage along with the U.S. coastal states. With the potential increase in hurricane intensity in changing climate conditions, the impacts of hurricanes are expected to be severer. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than intended. For the development of proper hurricane risk management strategies, it is crucial to investigate the climate change impact on hurricane risk. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims at investigating the climate change impact on hurricane wind and rain-ingress risk and freshwater flood risk on residential buildings across the southeastern U.S. coastal states. To address the challenge of computational inefficiency, surrogate models are developed using machine learning techniques for evaluating wind and freshwater flood losses of simulated climate-dependent hurricane scenarios. It is found that climate change impact varies by region and has a more significant influence on wind and rain-ingress damage, while both increases in wind and flood risks are not negligible.
Issue Date:2021-04-15
Rights Information:Copyright 2021 Chi-Ying Lin
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05

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