使用多式联运数据和深度神经网络方法预测野火的传播
Dmitrii Shadrin1, Svetlana Illarionova2, Fedor Gubanov1,3
1Skolkovo Institute of Science and Technology, Moscow, Russia, 121205.
Scientific reports
|January 31, 2024
概括
这项研究使用人工智能 (AI) 和遥感数据来预测野火在15天内蔓延. 关键因素包括风向和土地覆盖,显示大规模火灾管理的希望.
科学领域:
- 环境科学 环境科学
- 地理空间分析是什么
- 人工智能的人工智能
背景情况:
- 对于大型国家来说,野火蔓延的预测至关重要,但地面监测是不切实际的.
- 遥感数据为全球野火监测提供了可行的解决方案.
- 现有的方法通常集中在使用无人机数据的短期预测上.
研究的目的:
- 利用地理空间数据和机器学习开发一个有效的管道,用于大规模野火传播预测.
- 预测火灾在1到5天的时间里蔓延.
- 为了确定影响野火行为的重要特征.
主要方法:
- 一个基于MA-Net架构的神经网络模型被训练.
- 使用了包括空间分布在内的环境和气候数据.
- 进行了特征重要性分析,以了解促成因素.
主要成果:
- 该模型在15天内实现了0.640.68的F1得分,用于预测的烧伤区域.
- 风向和土地覆盖参数被确定为最重要的特征.
- 这项研究是在俄罗斯北部地区进行的.
结论:
- 基于地理空间数据的AI方法可以有效地预测大规模野火的蔓延.
- 马网模型对支持应急系统和决策充满希望.
- 该方法可适应在其他地区使用.
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