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arXiv Machine Learning By Arin Gopakumar, Marco Pannozzo

WildfireSpreadBench: The Metric Decides the Model in Wildfire Spread Prediction

Read the original on arXiv Machine Learning →

WildfireSpreadBench evaluates machine‑learning models for predicting next‑day wildfire spread, comparing five discriminative and one generative architecture on the WildfireSpreadTS dataset. The study shows that model rankings differ markedly when using Average Precision versus threshold‑dependent metrics such as F1 and IoU, revealing three distinct prediction profiles—over‑predicting, balanced, and under‑predicting—that AP alone cannot distinguish. Expanding input channels modestly affected AP, underscoring that AP may favor models with predictions poorly suited for operational use.

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