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预测城市排水系统的实时水位的替代方案
Pin-Chun Huang1, Kwan Tun Lee1
1Department of Harbor and River Engineering, National Taiwan Ocean University, Taiwan.
Journal of environmental management
|October 1, 2023
概括
本研究引入了一种AI模型来预测城市洪水水位,增强风暴水管理模型 (SWMM) 以实时预测洪水和改进城市排水管理.
科学领域:
- 环境工程 环境工程
- 城市水文学 城市水文学
- 人工智能的人工智能
背景情况:
- 风暴水管理模型 (SWMM) 被广泛用于风暴水流水分析.
- 集成的1D/1D SWMM模型改善了城市排水管理,但需要提高实时洪水预报的执行效率.
- 准确预测下水道和街道水位对于城市洪水管理至关重要.
研究的目的:
- 开发一种替代的人工智能模型,用于在暴雨期间预测城市下水道和街道排水系统的水位.
- 提高城市洪水预测模型的执行效率.
- 为城市排水系统提供实时预警的可靠方法.
主要方法:
- 利用动态神经元网络模型进行水位预测.
- 使用来自1D/1D SWMM模型的模拟结果作为培训标签来加强AI模型并解决数据稀缺问题.
- 实施了两阶段的培训程序,以应对下水道和街道网络中的各种洪水场景.
- 集成的水文条件,地形特性和排水网络关系用于AI模型培训.
主要成果:
- 拟议的AI方法在预测空间水深分布方面取得了令人满意的准确性.
- 人工智能模型有效地补充了数值SWMM模型,用于增强洪水预测.
- 两阶段的训练程序提高了模型处理各种洪水条件的能力.
结论:
- 开发的AI模型为预测暴雨期间的城市水位提供了可行和准确的替代方案.
- 这种方法增强了现有模型 (如SWMM) 的实时城市洪水预警系统的能力.
- 该方法为训练人工智能模型进行复杂的排水系统分析提供了一种新的方法.
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