基于历史火情划分非均匀区域，提升短期预测精度
Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction
- 用分水岭+K-means从火情数据中自动划分火灾区域
- 在6个法国省份测试中，平均交并比提升3%~6%
- 计算快、可并行，适合实际预警系统部署
传统野火预测模型通常将研究区域划分为统一网格，忽略了点火事件的空间异质性。本文挑战这一范式，指出数据离散化方式的重要性甚至超过模型选择。提出一种无监督的火区分割算法，结合分水岭检测与K-means聚类，直接从历史火情模式中定义预测单元。在六个法国省份及六种预测模型上的实验表明，该方法始终优于基于网格的方法，平均交并比（IoU）提升3%~6%，具体取决于空间尺度。该方法计算轻量（每配置<10秒），完全可并行。结果表明，优化空间离散化能为短期野火预测带来显著且可复现的性能提升。
原文摘要 · Abstract (English)
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.
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