Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods  

作　　者：Yuran Sun Shih‑Kai Huang Xilei Zhao 

机构地区：[1]Department of Civil and Coastal Engineering,University of Florida,Gainesville,FL 32611,USA [2]Department of Emergency Management and Public Administration,Jacksonville State University,Jacksonville,AL 36265,USA

出　　处：《International Journal of Disaster Risk Science》2024年第1期134-148,共15页国际灾害风险科学学报（英文版）

基　　金：supported by the National Science Foundation under Grant Nos.2303578,2303579, 05 27699,0838654,and 1212790;by an Early-Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences,Engineering,and Medicine

摘　　要：Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management.Current studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in practice.The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological factors.An enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new methodology.The findings show that,when predicting the households’ evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability.This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

关 键 词：Artifcial Intelligence(AI) Decision-making modeling Hurricane evacuation Interpretable machine learning Nonlinearity and interaction detection 

分 类 号：P429[天文地球—大气科学及气象学]