Evac-Cast: An Interpretable Machine-Learning Framework for Evacuation Forecasts Across Hurricanes and Wildfires
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| Format: | Preprint |
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2025
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| _version_ | 1866912515083993088 |
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| author | Li, Bo Liu, Chenyue Mostafavi, Ali |
| author_facet | Li, Bo Liu, Chenyue Mostafavi, Ali |
| contents | Evacuation is critical for disaster safety, yet agencies lack timely, accurate, and transparent tools for evacuation prediction. This study introduces Evac-Cast, an interpretable machine learning framework that predicts tract-level evacuation rates using over 20 features derived from four dimensions: hazard intensity, community vulnerability, evacuation readiness, and built environment. Using an XGBoost model trained on multi-source, large-scale datasets for two hurricanes (Ian 2022, Milton 2024) and two wildfires (Kincade 2019, Palisades--Eaton 2025), Evac-Cast achieves mean absolute errors of 4.5% and 3.5% for hurricane and wildfire events, respectively. SHAP analysis reveals a consistent feature importance hierarchy across hazards, led by hazard intensity. Notably, the models perform well without explicit psychosocial variables, suggesting that macro-level proxies effectively encode behavioral signals traditionally captured through time-consuming surveys. This work offers a survey-free, high-resolution approach for predicting and understanding evacuation in hazard events, which could serve as a data-driven tool to support decision-making in emergency management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evac-Cast: An Interpretable Machine-Learning Framework for Evacuation Forecasts Across Hurricanes and Wildfires Li, Bo Liu, Chenyue Mostafavi, Ali Physics and Society Evacuation is critical for disaster safety, yet agencies lack timely, accurate, and transparent tools for evacuation prediction. This study introduces Evac-Cast, an interpretable machine learning framework that predicts tract-level evacuation rates using over 20 features derived from four dimensions: hazard intensity, community vulnerability, evacuation readiness, and built environment. Using an XGBoost model trained on multi-source, large-scale datasets for two hurricanes (Ian 2022, Milton 2024) and two wildfires (Kincade 2019, Palisades--Eaton 2025), Evac-Cast achieves mean absolute errors of 4.5% and 3.5% for hurricane and wildfire events, respectively. SHAP analysis reveals a consistent feature importance hierarchy across hazards, led by hazard intensity. Notably, the models perform well without explicit psychosocial variables, suggesting that macro-level proxies effectively encode behavioral signals traditionally captured through time-consuming surveys. This work offers a survey-free, high-resolution approach for predicting and understanding evacuation in hazard events, which could serve as a data-driven tool to support decision-making in emergency management. |
| title | Evac-Cast: An Interpretable Machine-Learning Framework for Evacuation Forecasts Across Hurricanes and Wildfires |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2508.00650 |