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从基于物理过程的方法到机器学习方法的演变,用于预测城市洪水:文献综述
Md Shike Bin Mazid Anik1, Chunjiang An1, S Samuel Li1
1Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8 Canada.
Environmental systems research
|July 22, 2025
概括
城市洪水是一个越来越令人担忧的问题. 本文回顾了洪水预测方面的进展,从物理模型到人工智能驱动的系统,强调了为城市恢复能力提供综合性,社区包容性的解决方案的需要.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 人工智能的人工智能
背景情况:
- 城市洪水是城市化,气候变化和基础设施老化带来的日益严重的威胁.
- 传统的洪水预测模型 (基于物理过程的模型 - PPBMs) 与快速的城市变化作斗争.
- 之前的研究还没有完全探索人工智能驱动的,实时的和社区集成的洪水预测方法.
研究的目的:
- 为了回顾过去二十年城市洪水预测的演变.
- 要突出从传统模型向先进的人工智能和数据驱动技术的转变.
- 确定挑战,并提出提高城市抗洪能力的未来方向.
主要方法:
- 基于物理过程的模型 (PPBMs) 的审查.
- 整合地理信息系统 (GIS),LiDAR和卫星图像用于绘图和规划.
- 人工智能 (AI) 和机器学习 (ML) 的应用,以使用多种数据源 (天气,传感器,社交媒体) 进行更快,更准确的预测.
- 探索新兴工具,如物联网 (IoT) 设备,深度学习和混合模型.
主要成果:
- 人工智能和机器学习显著提高了洪水预测的准确性和速度.
- 先进的工具可以整合各种数据流,包括众包信息.
- 尽管取得了进展,但在数据可用性,传感器基础设施以及与城市规划和社区努力的模型整合方面仍然存在重大挑战.
结论:
- 未来的城市洪水预测系统必须整合人工智能,水力学,地理信息系统和实时监测.
- 适应城市变化和社区投入对于有效的洪水解决方案至关重要.
- 开源工具,公众教育和改进的规划对于建设抗洪城市至关重要.
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