Predicting Post-Wildfire Debris Flow Onset Using Machine Learning Models on Multi-Parameter Experimental Data

The increasing frequency of wildfires, particularly in wildland-urban interface zones, has elevated the hazard posed by post-wildfire debris flows. Following combustion, surface and near-surface soils often become hydrophobic, a phenomenon that occurs predominantly on sand-based hillslopes. Rainwater and eroded soil accumulate downslope, frequently evolving into destructive debris flows. Soil hydrophobicity exacerbates erosion, distinguishing post-wildfire debris flows from natural debris flows in terms of intensity, duration, and destructive potential. Therefore, understanding the timing and conditions under which such debris flows initiate is critical. This initiation results from coupled effects among several key parameters: rainfall intensity (RI), slope gradient (δ), water entry value ( Ψ_wev ), and grain size (D50). Machine Learning (ML) techniques have become increasingly valuable in geotechnical engineering due to their ability to model complex systems without predefined assumptions. This study applied multiple ML algorithms: multiple linear regression (MLR), logistic regression (LR), support vector classifier (SVC), K-means clustering, and principal component analysis (PCA) to predict and classify outcomes from laboratory experiments that model field conditions using a rain device on various soils in sloped flumes. The results indicate that while MLR performs adequately in predicting total discharge, its accuracy in predicting erosion is limited, particularly for coarse sand. In contrast, LR and SVC achieve satisfactory accuracy in classifying failure outcomes, supported by clustering and dimensionality reduction techniques. Sensitivity analysis reveals that fine sand is highly susceptible to erosion, especially under low-intensity, prolonged rainfall. Furthermore, the first ten minutes of high-intensity rainfall emerge as the most critical period for discharge generation and slope failure. Collectively, these findings underscore the potential of machine learning to enhance post-wildfire hazard assessment and inform emergency response planning.