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适应性紧急管理的综合洪水风险预警:IFloPhy框架连接机器学习和物理模型
Jilin Huang1, Lujia Li1,2, Zhichao Li1,2,3
1School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China.
概括
一个新的综合框架,IFloPhy,结合了机器学习,物理模型和社会数据,用于自适应应急管理 (AEM). 该系统提供了增强的早期洪水风险预警,提高了动态响应和预测准确度.
科学领域:
- 水文和水资源水文与水资源
- 环境工程 环境工程
- 数据科学数据科学数据科学
背景情况:
- 全球洪水风险的增加需要先进的自适应应急管理 (AEM) 框架.
- 传统的单一预警系统缺乏有效的洪水管理所需的动态响应和预测准确性.
- 综合多种数据来源对于全面的洪水风险评估至关重要.
研究的目的:
- 开发IFloPhy (集成机器学习和河流物理模型) 框架,用于AEM下的洪水风险预警.
- 通过整合机器学习,物理模型和社会数据来克服传统预警系统的局限性.
- 提高洪水风险管理中的动态响应能力和预测准确性.
主要方法:
- 机器学习算法与物理模型的集成 (可变透能力,InfoWorks-ICM).
- 纳入社会数据和实时卫星数据,以进行全面的洪水风险评估.
- 开发动态洪水跟踪和预警机制的多维集成设计.
主要成果:
- 与传统系统相比,IFloPhy框架展示了增强的动态响应能力和预测准确性.
- 实时数据集成显著降低了预测不确定性,建立了立即预警机制.
- 在圣伊莎贝尔盆地成功部署和验证,即使在数据稀缺的地区.
结论:
- IFloPhy提供了一种全新的,综合的洪水风险应急管理方法,改进了AEM.
- 该框架通过考虑自然和社会经济因素,提供全面和互动的洪水风险评估.
- IFloPhy介绍了用于有效管理洪水风险的新技术和见解,特别是在具有挑战性的环境中.
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