面向数据驱动的野火后泥石流预测的可解释基准测试

中文摘要 AI 辅助

野火后泥石流（PFDF）是一种破坏性的含沉积物灾害，当强降雨袭击近期燃烧的地形时，会使山坡失稳，威胁基础设施、当地经济和社区安全。数据驱动方法已被提出，用于直接从历史PFDF观测中学习预测模式。然而，当前数据驱动PFDF预测的研究格局在特征空间、模型架构和评估协议方面高度碎片化，使得严格比较和科学见解的推导变得困难。此外，现有研究缺乏对触发PFDF的异质因素（如气象条件、地形特征、土壤特性和燃烧严重程度）相对重要性的系统调查。为解决这些局限性，我们提出了一个统一的数据驱动PFDF预测基准，能够在不同模型和特征配置下进行公平和全面的评估。此外，为了更好地理解PFDF形成的潜在驱动因素，我们提出了一种基于强化学习的特征选择框架，该框架识别那些扰动使正负事件无法区分的因素，从而发现跨区域的PFDF发生的区域潜在机制。我们的代码和基准可在以下网址公开获取：此HTTPS URL。

英文摘要

Post-wildfire debris flows (PFDFs) are destructive sediment-laden hazards triggered when intense rainfall strikes recently burned terrain, destabilizing hillslopes and threatening infrastructure, local economies, and community safety. Data-driven methods have been proposed to learn predictive patterns directly from historical PFDF observations. However, the current research landscape of data-driven PFDF prediction remains highly fragmented across feature spaces, model architectures, and evaluation protocols, making rigorous comparison and the derivation of scientific insights difficult. Moreover, existing studies lack a systematic investigation into the relative importance of heterogeneous factors (e.g., meteorological conditions, terrain characteristics, soil properties, and burn severity) in triggering PFDF. To address these limitations, we present a unified benchmark for data-driven PFDF prediction, enabling fair and comprehensive evaluation across diverse models and feature configurations. Furthermore, to better understand the underlying drivers of PFDF formation, we propose a reinforcement learning-based feature selection framework that identifies factors whose perturbations render positive and negative events indistinguishable, thereby discovering the regional underlying mechanisms of PFDF occurrence across regions. Our code and benchmark are publicly available at https://github.com/KINDLab-Fly/PFDF-Benchmark.

发表机构

• University of Georgia（佐治亚大学）
• University of Oregon（俄勒冈大学）

机构由 AI 辅助整理，请以论文原文为准。