Uncertainty Reduction Through Data Management in the Development, Validation, Calibration, and Operation of a Hurricane Vulnerability Model

作     者：Jean-Paul Pinelli Josemar Da Cruz Kurtis Gurley Andres Santiago Paleo-Torres Mohammad Baradaranshoraka Steven Cocke Dongwook Shin Jean-Paul Pinelli;Josemar Da Cruz;Kurtis Gurley;Andres Santiago Paleo-Torres;Mohammad Baradaranshoraka;Steven Cocke;Dongwook Shin

作者机构：College of Engineering and ScienceFlorida Institute of TechnologyMelbourneFL 32901USA Civil and Coastal EngineeringUniversity of FloridaGainesvilleFL 32611USA Center for Ocean-Atmospheric Prediction Studies(COAPS)Florida State UniversityTallahasseeFL 32306USA 

出 版 物：《International Journal of Disaster Risk Science》 (国际灾害风险科学学报（英文版）)

年 卷 期：2020年第11卷第6期

页      面：790-806页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：The Florida Office of Insurance Regulation(FLOIR)provided financial support 

主　　题：Catastrophe modeling Data augmentation and integration Florida Public Hurricane Loss Model(FPHLM) Uncertainty reduction Vulnerability model 

摘      要：Catastrophe models estimate risk at the intersection of hazard,exposure,and *** of these areas requires diverse sources of data,which are very often incomplete,inconsistent,or missing *** poor quality of the data is a source of epistemic uncertainty,which affects the vulnerability models as well as the output of the catastrophe *** article identifies the different sources of epistemic uncertainty in the data,and elaborates on strategies to reduce this uncertainty,in particular through identification,augmentation,and integration of the different types of *** challenges are illustrated through the Florida Public Hurricane Loss Model(FPHLM),which estimates insured losses on residential buildings caused by hurricane events in *** define the input exposure,and for model development,calibration,and validation purposes,the FPHLM teams accessed three main sources of data:county tax appraiser databases,National Flood Insurance Protection(NFIP)portfolios,and wind insurance *** data from these different sources were reformatted and processed,and the insurance databases were separately cross-referenced at the county level with tax appraiser *** FPHLM hazard teams assigned estimates of natural hazard intensity measure to each insurance *** efforts produced an integrated and more complete set of building descriptors for each policy in the NFIP and wind *** article describes the impact of these uncertainty reductions on the development and validation of the vulnerability models,and suggests avenues for data *** learned should be of interest to professionals involved in disaster risk assessment and management.