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ML-Based Fire Cause Prediction: Integrating Population Mobility and Meteorological Data
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DOI：10.1016/j.ijdrr.2026.106257.png)
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Abstract
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• Some fires can be effectively understood using ML models to estimate their probable cause. • ML estimation can help to retrospectively populate the database and assign a probability of cause. • Using leave-one-out validation can identify hotspots of social anomalies in a smaller areas. • With hierarchical clustering, we can estimate the model’s behavior in each district and thus target appropriate population groups.
Keywords:
wildfire
fire risk
machine learning
clustering
GIS
spatial analysis
GFS model
population mobility
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