Suchergebnisse

Abstract
Machine learning was applied to predict evacuation rates for all census tracts affected by Hurricane Laura. The evacuation ground truth was derived from cellular telephone–based mobility data. Twitter data, census data, geographical data, COVID-19 case rates, the social vulnerability index from the Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), and relevant weather and physical data were used to do the prediction. Random forests were found to perform well, with a mean absolute percent error 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.

Significance Statement
This study evaluates the usefulness of Twitter data, COVID-19 case rates, and the social vulnerability index from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry in predicting evacuation rates during Hurricane Laura, in the context of other relevant geographic, and weather-related variables. All three are found to be useful, to different extents, and this work suggests important directions for future research in understanding the reasons behind their relevance to predicting evacuation rates.