Predicting Next-Day Wildfire Spread with Time Series and Attention

• URL: http://arxiv.org/abs/2502.12003v1
• Date: Mon, 17 Feb 2025 16:41:46 GMT
• Title: Predicting Next-Day Wildfire Spread with Time Series and Attention
• Authors: Saad Lahrichi, Jesse Johnson, Jordan Malof,
• Abstract summary: We investigate a transformer-based model, termed the SwinUnet, for next-day wildfire prediction.<n>We benchmark Swin-based models against several current state-of-the-art models on WildfireSpreadTS.<n>We find that, with the proper modifications, SwinUnet achieves state-of-the-art accuracy on next-day prediction for both the single-day and multi-day scenarios.
• Score: 1.6385815610837162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research has demonstrated the potential of deep neural networks (DNNs) to accurately predict next-day wildfire spread, based upon the current extent of a fire and geospatial rasters of influential environmental covariates e.g., vegetation, topography, climate, and weather. In this work, we investigate a recent transformer-based model, termed the SwinUnet, for next-day wildfire prediction. We benchmark Swin-based models against several current state-of-the-art models on WildfireSpreadTS (WFTS), a large public benchmark dataset of historical wildfire events. We consider two next-day fire prediction scenarios: when the model is given input of (i) a single previous day of data, or (ii) five previous days of data. We find that, with the proper modifications, SwinUnet achieves state-of-the-art accuracy on next-day prediction for both the single-day and multi-day scenarios. SwinUnet's success depends heavily upon utilizing pre-trained weights from ImageNet. Consistent with prior work, we also found that models with multi-day-input always outperformed models with single-day input.

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