构建俄罗期大尺度火灾数据集，用机器学习揭示火情规律
Advancing Eurasia Fire Understanding Through Machine Learning Techniques
- 构建覆盖13个月的俄罗斯开放火灾数据集，含气象与火情信息
- 机器学习识别出不同生态系统中火灾行为的关键影响因素
- 为欧亚大陆火情预测提供数据支持，适合气候与灾害研究者
现代火灾管理系统越来越多依赖卫星数据和天气预报，但因专有权限限制，全面数据获取仍受限。尽管野火具有重要生态意义，大范围、跨区域研究仍受制于数据匮乏。俄罗斯多样化的生态系统在塑造欧亚大陆火灾动态中起关键作用，却长期缺乏深入研究。本研究通过引入一个开放获取的数据集，记录了详细的火灾事件及其对应的气象条件。该数据集是目前可用于俄罗斯野火分析最全面的之一，涵盖连续13个月的观测数据。利用机器学习技术，我们开展了探索性数据分析并构建了预测模型，以识别不同火灾类别和生态系统中的关键火情行为模式。结果表明，环境因子组合对火灾发生与蔓延具有决定性影响。本研究提升了对欧亚大陆火灾动态的理解，有助于在环境变化背景下发展更有效的数据驱动型主动火灾管理策略。
原文摘要 · Abstract (English)
Modern fire management systems increasingly rely on satellite data and weather forecasting; however, access to comprehensive datasets remains limited due to proprietary restrictions. Despite the ecological significance of wildfires, large-scale, multi-regional research is constrained by data scarcity. Russian diverse ecosystems play a crucial role in shaping Eurasian fire dynamics, yet they remain underexplored. This study addresses existing gaps by introducing an open-access dataset that captures detailed fire incidents alongside corresponding meteorological conditions. We present one of the most extensive datasets available for wildfire analysis in Russia, covering 13 consecutive months of observations. Leveraging machine learning techniques, we conduct exploratory data analysis and develop predictive models to identify key fire behavior patterns across different fire categories and ecosystems. Our results highlight the critical influence of environmental factor patterns on fire occurrence and spread behavior. By improving the understanding of wildfire dynamics in Eurasia, this work contributes to more effective, data-driven approaches for proactive fire management in the face of evolving environmental conditions.
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