用机器学习预测火灾后泥流发生时机，关键在前10分钟强降雨
Predicting the post-wildfire mudflow onset using machine learning models on multi-parameter experimental data
- 融合雨强、坡度、土壤粒径等多参数实验数据，训练多种机器学习模型
- 前10分钟高强度降雨最易引发泥流，细沙土在低强度长时雨中更易侵蚀
- 适合灾害预警与应急响应人员，可提升火灾后地质风险评估精度
火灾后泥流危害加剧，因燃烧使表层土壤变疏水，尤其在砂质斜坡上。雨水与侵蚀土壤顺坡下冲，形成强度高、持续时间长、破坏力大的泥流。本研究通过实验室雨雾装置模拟不同土壤（含粒径D50）在斜坡上的实况，采集雨强（RI）、坡度、入渗值等参数，应用多元线性回归（MLR）、逻辑回归（LR）、支持向量分类器（SVC）、K均值聚类与主成分分析（PCA）等机器学习方法建模。结果表明：MLR能较好预测总出流，但对粗砂侵蚀预测偏差较大；LR与SVC在失败状态分类上表现良好，经聚类与降维验证；敏感性分析显示细砂土在低强度、长时降雨下极易侵蚀；最关键的是，前10分钟高强度降雨是触发泥流和出流的决定性阶段。研究揭示了机器学习在火灾后灾害评估中的潜力。
原文摘要 · Abstract (English)
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.
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