Interactive Web Dashboard for Post-Wildfire Debris Flow Risk Assessment through a validated ML model

Extreme rainfall-induced debris flows in post-wildfire watersheds across the western United States pose critical threats to downstream communities and infrastructure. An accurate and a prompt prediction of potential risk is vital for effective mitigation and emergency response. To address this, we present a comprehensive machine learning (ML) framework to enhance prediction of debris flow probabilities. Our methodology integrates remotely sensed soil moisture data alongside rainfall intensity, determining how antecedent wetness influences the rainfall threshold to trigger debris flows. 

The ML model is trained and tested on several historical California wildfire events, using approximately 50 geomorphological, hydrological,  geological, and other fire-related parameters extracted and processed from high-resolution digital elevation models, satellite-derived burn severity products, and hydro-meteorological reanalysis datasets. This multi-parameter integration achieves superior prediction accuracy by capturing the complex interaction between meteorological triggers and surface conditions. To facilitate operational deployment, we are developing an interactive web-based dashboard that enables real-time debris flow probability assessment.  

The dashboard acts as a cyber infrastructure where users  simply input fire perimeter boundaries and select key parameters options, such as burn severity. The framework then automatically retrieves the necessary environmental data including near-real-time soil moisture and precipitation inputs to generate probabilistic hazard mapping. Using our tool, emergency managers and stakeholders benefit from enhanced decision-support for post-wildfire risk assessment.