Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction
Quinn Ledingham, Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring, Saeid Taleghanidoozdoozan, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu
cs.LG, cs.CV
Submitted: 2026-08-05
Code: https://github.com/PriorLabs/tabpfn-extensions
License: http://creativecommons.org/licenses/by/4.0/
The gist: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.
Terminology
Abstract
Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas. However, identifying reliable machine learning models is complicated by overlapping debris-flow and non-debris-flow events in feature space, the need for model interpretability, and limited training data. This paper addresses these challenges through a systematic evaluation of machine learning models in terms of predictive performance, feature importance, and synthetic data augmentation. Using basin-scale observations of post-wildfire debris-flow events across the western United States, we compare 15 models, including the Tabular Prior-Data Fitted Network (TabPFN). Repeated stratified cross-validation shows that TabPFN achieves the highest unaugmented performance with a threat score of 0.637, closely followed by the best tree-based models. SHapley Additive exPlanations (SHAP) are used to identify the features driving predictions, revealing that short-duration rainfall intensity and storm accumulation consistently rank highest, while burn severity and terrain features contribute less. We further evaluate synthetic data augmentation using TabPFN-generated samples to address the scarcity of debris-flow observations. Synthetic augmentation improves the performance of all models except CNN, with the largest mean threat score increase of +0.041 among the deep learning models. By combining rigorous model benchmarking, interpretable feature analysis, and synthetic data augmentation, this work provides a comprehensive framework for improving post-wildfire debris-flow prediction.
Sources
- xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
- TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
- Why do tree-based models still outperform deep learning on tabular data?
- Mamba: Linear-Time Sequence Modeling with Selective State Spaces
- TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
- A Unified Approach to Interpreting Model Predictions
- Explainable AI for Trees: From Local Explanations to Global Understanding
- Transformers Can Do Bayesian Inference
- "Why Should I Trust You?": Explaining the Predictions of Any Classifier
- Interpretable Machine Learning for TabPFN
- Modeling Tabular data using Conditional GAN
- Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
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