探索地理空间数据和机器学习算法,以进行强大的野火发生预测
Svetlana Illarionova1, Dmitrii Shadrin2, Fedor Gubanov2
1Skolkovo Institute of Science and Technology, Moscow, Russia, 121205. s.Illarionova@skoltech.ru.
Scientific reports
|March 29, 2025
概括
准确的野火发生预测需要为每个地区量身定制的机器学习 (ML) 模型. 将环境,地理空间和人为数据与ML集成,可以提高应急响应系统的预测准确度.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 生态系统管理 生态系统管理
背景情况:
- 野火对生态系统产生重大影响,需要及时采取干预策略.
- 现有的野火预报依赖于天气数据,但使用全面数据的先进方法提供了优势.
- 由于不同的环境和地理因素,预测野火的发生是复杂的,缺乏统一的方法.
研究的目的:
- 探索机器学习 (ML) 算法用于野火发生预测的潜力.
- 开发一个统一的数据采集和ML模型开发管道,使用不同的环境参数.
- 评估各种ML算法在不同地区预测野火事件的性能.
主要方法:
- 利用了10年来俄罗斯中部17000多起野火事件的综合数据集.
- 应用了一系列ML算法,包括随机森林,XGBoost,自动编码器,ConvLSTM,注意力多层感知器和RegNetX.
- 开发了一个统一的数据采集和ML管道,解决不平衡的时空数据的挑战.
主要成果:
- 获得了F1分数从0.7到0.87不等,表明了显著的预测潜力.
- 证明了考虑到当地环境特征的区域特定的ML模型,可以获得更高的准确性.
- 强调了整合气象,地理空间和人类数据的有效性,以改善预测.
结论:
- 定制的ML模型对于在不同地理区域准确预测野火事件至关重要.
- 开发的管道和经过测试的算法显示了改善野火管理和应急响应的前景.
- 集成先进的数据源和人工智能技术可以大大改善对野火减缓工作的决策.
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