用时序数据提升野火蔓延预测精度，新基准扩展至更广时空范围。
Improved Wildfire Spread Prediction with Time-Series Data and the WSTS+ Benchmark
- 对比多种数据驱动模型，发现时序输入能显著提升预测准确率。
- 在WSTS+新基准上实现当前最优性能，覆盖更多历史年份与地理区域。
- 新基准数据量翻倍，适合研究长期野火演化与跨区域建模的学者使用。
近期研究已证明深度神经网络可基于单日或连续T日的高维输入数据，准确预测某日的野火蔓延情况。本文首次在受控条件下系统评估大量现有数据驱动建模策略，识别出最佳方法，并在大型公开基准WildfireSpreadTS（WSTS）上实现了单日与多日输入场景下的当前最优（SOTA）精度。与先前研究一致，使用时序输入的模型表现最佳，表明该方向具有重要研究价值。此外，我们通过引入额外四年历史野火数据构建了新基准WSTS+，使数据覆盖的独立年数翻倍，地理范围扩大，据我们所知，这是目前最大的基于时序数据的野火蔓延预测公共基准。
原文摘要 · Abstract (English)
Recent research has demonstrated the potential of deep neural networks (DNNs) to accurately predict wildfire spread on a given day based upon high-dimensional explanatory data from a single preceding day, or from a time series of T preceding days. For the first time, we investigate a large number of existing data-driven wildfire modeling strategies under controlled conditions, revealing the best modeling strategies and resulting in models that achieve state-of-the-art (SOTA) accuracy for both single-day and multi-day input scenarios, as evaluated on a large public benchmark for next-day wildfire spread, termed the WildfireSpreadTS (WSTS) benchmark. Consistent with prior work, we found that models using time-series input obtained the best overall accuracy, suggesting this is an important future area of research. Furthermore, we create a new benchmark, WSTS+, by incorporating four additional years of historical wildfire data into the WSTS benchmark. Our benchmark doubles the number of unique years of historical data, expands its geographic scope, and, to our knowledge, represents the largest public benchmark for time-series-based wildfire spread prediction.
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