桥梁空间时间野火预测和决策建模使用变压器网络和模糊推理系统
1Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.
MethodsX
|July 30, 2025
概括
本研究介绍了一种结合深度学习和模糊逻辑的AI系统,用于准确地预测野火和可解释的应对计划. 它可以在不确定的条件下加强决策,改善野火管理策略.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 野火对生态系统和人类人口构成越来越大的风险.
- 现有的人工智能模型缺乏解释性,并且在野火预测中扎着不确定性.
- 当前的系统往往无法将领域知识整合到可行的干预措施中.
研究的目的:
- 开发一种可解释的AI系统,用于野火预测和应对计划.
- 将深度学习与模糊逻辑集成在一起,以在不确定性下加强决策.
- 提高野火管理工具的准确性和透明度.
主要方法:
- 使用加拿大火灾传播数据集与哨兵,ERA5和SRTM数据.
- 开发了一个基于变压器的模型,用于时空火灾预测.
- 集成了一个基于规则的模糊系统 (FRBS),用于生成可解释的响应策略.
主要成果:
- 获得了92.9%的F1分数和94.8%的准确性,表现优于基线模型.
- 使用多个来源的环境数据证明了卓越的性能.
- 成功地将复杂的预测转化为可操作的消防管理规则.
结论:
- 混合人工智能系统提供准确的野火预测和可解释的响应计划.
- 这种方法增强了对有效野火管理的不确定性意识推理.
- 该模型在人工智能驱动的环境风险评估中取得了重大进展.
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