用于改进短期野火预测的数据驱动火区分割方法
Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction
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中文总结 AI 辅助
该研究针对传统野火预测模型均匀网格离散化的缺陷，提出结合分水岭检测与K-means聚类的无监督火区分割算法，经法国6省及6种模型验证，其预测性能优于网格方法，计算高效且可并行化。
中文摘要 AI 辅助
野火预测模型通常将研究区域离散为均匀网格，忽略了火源的异质空间分布。我们对这一范式提出挑战，证明数据的离散方式比所使用的模型更为重要。我们提出一种无监督火区分割算法，结合分水岭检测与K-means聚类，直接从历史火灾模式定义预测单元。在法国6个省及6种预测模型上开展的实验显示，火区分割始终优于基于网格的方法，依据空间尺度不同，平均交并比（IoU）提升3%至6%。该方法计算量轻（每种配置耗时不足10秒）且完全可并行化。我们的结果表明，优化空间离散化可为短期野火预测带来显著且可复现的性能提升。
英文摘要
Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions. We challenge this paradigm by showing that how data is discretized matters more than which model is used. We propose an unsupervised fire-zone segmentation algorithm combining watershed detection with K-means clustering to define prediction units directly from historical fire patterns. Experiments across six French departments and six forecasting models show that fire-zone segmentation consistently outperforms grid-based approaches, with mean IoU improvements of +3--6% depending on spatial scale. The method is computationally lightweight (<10s per configuration) and fully parallelizable. Our results demonstrate that optimizing spatial discretization yields significant, reproducible performance gains for short-term wildfire forecasting.
发表机构
- Université Maris et Louis Pasteur（马里斯与路易·巴斯德大学）
- SAD Marketing（SAD营销公司）
机构由 AI 辅助整理，请以论文原文为准。
