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Characterizing and Predicting Wildfire Evacuation Behavior: A Dual-Stage ML Approach

arXiv – CS AI|Sazzad Bin Bashar Polock, Anandi Dutta, Subasish Das|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers used machine learning techniques to analyze wildfire evacuation behavior patterns from survey data across California, Colorado, and Oregon. The study found that transportation mode during evacuations can be reliably predicted from household characteristics, while evacuation timing remains difficult to predict due to dynamic fire conditions.

Key Takeaways

→Machine learning successfully identified distinct behavioral subgroups based on vehicle access, disaster planning, technology resources, pet ownership, and residential stability.
→Transportation mode during wildfire evacuations can be predicted with high reliability using household characteristic data.
→Evacuation timing remains challenging to classify due to dependence on real-time, dynamic fire conditions.
→The dual-stage ML approach combining unsupervised and supervised methods revealed consistent patterns across multiple states.
→Findings can support targeted preparedness strategies, resource allocation, and more equitable emergency planning.

#machine-learning #wildfire #evacuation #prediction #emergency-planning #clustering #behavioral-analysis #disaster-response

Read Original →via arXiv – CS AI

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