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火灾强度和spRead预测 (FIRA):一个基于机器学习的火灾传播预测模型用于空气质量预测应用程序

Wei-Ting Hung^1,2,3, Barry Baker¹, Patrick C Campbell^1,2,3

¹Air Resources Laboratory National Oceanic and Atmospheric Administration College Park MD USA.

|March 24, 2025

概括

一个新的机器学习模型,火灾强度和火灾预测 (FIRA),准确地预测野火的蔓延和强度. 这通过提供动态的火灾数据来改善空气质量模型,从而更好地预测烟雾对公共健康的影响.

科学领域:

大气科学大气科学
环境科学环境科学
计算机科学计算机科学

背景情况:

野火释放出危险的污染物,影响空气质量和公共健康.
当前空气质量预测 (AQF) 模型往往缺乏实时火灾传播动态,限制了预测准确性.

研究的目的:

开发一种新的机器学习 (ML) 模型,火灾强度和spRead forecAst (FIRA),用于预测野火蔓延和强度.
通过整合空间分布和火焰辐射功率 (FRP) 等动态火灾特征来增强AQF模型.

主要方法:

使用2020 CONUS和加利福尼亚火灾历史数据开发了FIRA ML模型.
应用FIRA的FRP预测作为输入到合烟雾 (UFS-Smoke) 模型的统一预测系统.
通过2020年9月加利福尼亚州的火灾案例评估FIRA与近实时消防产品的性能.

主要成果:

菲拉在捕捉火灾传播 (R平方≈0.7) 和空间相似性 (≈95%) 方面表现出强的表现.
FIRA的预测与UFS-Smoke模拟显示出很好的一致性,表明未来火灾活动的准确表示.
与基线UFS-Smoke模型相比,缩放的FIRA预测显著改善了气溶光学深度,3D烟雾含量和表面PM2.5度的预测.

结论:

FIRA模型为AQF应用程序预测野火行为提供了显著的进步.
将FIRA集成到AQF系统中,可以更准确地预测野火烟雾对空气质量和公共卫生的影响.
虽然FIRA可能低估了火灾强度,但应用缩放因子有效地减轻了这种不确定性.

关键词:

监测,预测,预测和预测的使用.污染:城市和地区污染.野生野火模型的野火模型

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