Predicting the post-wildfire mudflow onset using machine learning models on multi-parameter experimental data

TL;DR

This study employs multiple machine learning models to predict and classify post-wildfire mudflow onset using experimental data, identifying key parameters like rain intensity, slope, and soil grain size that influence debris flow hazards.

Contribution

It introduces the application of diverse ML algorithms to model complex post-wildfire debris flow conditions based on laboratory experiments, providing insights into critical parameters affecting mudflow onset.

Findings

MLR predicted total discharge effectively but struggled with erosion accuracy.

LR and SVC accurately classified failure outcomes.

Fine sand is highly susceptible to erosion under low-intensity rainfall.

Abstract

Post-wildfire mudflows are increasingly hazardous due to the prevalence of wildfires, including those on the wildland-urban interface. Upon burning, soil on the surface or immediately beneath becomes hydrophobic, a phenomenon that occurs predominantly on sand-based hillslopes. Rainwater and eroded soil blanket the downslope, leading to catastrophic debris flows. Soil hydrophobicity enhances erosion, resulting in post-wildfire debris flows that differ from natural mudflows in intensity, duration, and destructiveness. Thus, it is crucial to understand the timing and conditions of debris-flow onset, driven by the coupled effects of critical parameters: varying rain intensities (RI), slope gradients, water-entry values, and grain sizes (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 applies 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. While MLR effectively predicted total discharge, erosion predictions were less accurate, especially for coarse sand. LR and SVC achieved good accuracy in classifying failure outcomes, supported by clustering and dimensionality reduction. Sensitivity analysis revealed that fine sand is highly susceptible to erosion, particularly under low-intensity, long-duration rainfall. Results also show that the first 10 minutes of high-intensity rain are most critical for discharge and failure. These findings highlight the potential of ML for post-wildfire hazard assessment and emergency response planning.