飓风劳拉期间疏散率预测：天气预报、推特和新冠肺炎

IF 1.9 4区地球科学 Q3 ENVIRONMENTAL STUDIES

Weather Climate and Society Pub Date : 2022-12-19 DOI:10.1175/wcas-d-22-0006.1

Anna E. Brower, Bianca G. Corpuz, Balaji Ramesh, B. Zaitchik, J. Gohlke, S. Swarup

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引用次数: 1

摘要

机器学习被应用于预测受飓风劳拉影响的所有人口普查区的疏散率。疏散地面的真实情况来源于基于手机的移动数据。使用Twitter数据、Census数据、地理数据、COVID-19病例率、CDC/ATSDR社会脆弱性指数以及相关天气和物理数据进行预测。随机森林被发现表现良好，测试数据的MAPE为4.9%。使用Shapley加性解释分析特征对预测的重要性，发现以前的疏散、降雨预报、COVID-19病例率和Twitter数据的重要性排名很高。社会脆弱性指数也显示出与疏散率非常一致的关系，即较高的脆弱性始终意味着较低的疏散率。这些发现有助于飓风疏散准备和规划以及实时评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predictors of Evacuation Rates During Hurricane Laura: Weather Forecasts, Twitter, and COVID-19

Machine learning was applied to predict evacuation rates for all Census tracts affected by Hurricane Laura. The evacuation ground truthwas derived from cellphone-based mobility data. Twitter data, Census data, geographical data, COVID-19 case rates, the CDC/ATSDR social vulnerability index, and relevant weather and physical data were used to do the prediction. Random forests were found to perform well, with a MAPE of 4.9% on testing data. Feature importance for prediction was analyzed using Shapley additive explanations and it was found that previous evacuation, rainfall forecasts, COVID-19 case rates, and Twitter data rank highly in terms of importance. Social vulnerability indices were also found to show a very consistent relationship with evacuation rates, such that higher vulnerability consistently implies lower evacuation rates. These findings can help with hurricane evacuation preparedness and planning as well as real-time assessment.

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来源期刊

Weather Climate and Society METEOROLOGY & ATMOSPHERIC SCIENCES-

CiteScore

3.40

自引率

13.60%

发文量

审稿时长

>12 weeks

期刊介绍： Weather, Climate, and Society (WCAS) publishes research that encompasses economics, policy analysis, political science, history, and institutional, social, and behavioral scholarship relating to weather and climate, including climate change. Contributions must include original social science research, evidence-based analysis, and relevance to the interactions of weather and climate with society.