# Post-Wildfire Debris Flow Prediction

The work builds a unified benchmark for data-driven post-wildfire debris flow (PFDF) prediction, uses an LLM to convert operational weather forecast narratives into structured meteorological indicators and augments features with terrain and soil-moisture context, and proposes a reinforcement-learning feature-selection framework that identifies key drivers by perturbing features until positive and negative events become indistinguishable; learned models consistently outperform the fixed-coefficient USGS logistic-regression baseline, augmented features further improve most model families, and storm variables dominate globally while feature importance varies substantially across regions.

The work builds a unified benchmark for data-driven post-wildfire debris flow (PFDF) prediction, uses an LLM to convert operational weather forecast narratives into structured meteorological indicators and augments features with terrain and soil-moisture context, and proposes a reinforcement-learning feature-selection framework that identifies key drivers by perturbing features until positive and negative events become indistinguishable; learned models consistently outperform the fixed-coefficient USGS logistic-regression baseline, augmented features further improve most model families, and storm variables dominate globally while feature importance varies substantially across regions.