通过混合深度学习提高抗洪能力:对台北城市排水系统的先进预测
Li-Chiu Chang1, Ming-Ting Yang2, Fi-John Chang2
1Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City, 25137, Taiwan.
Journal of environmental management
|March 8, 2025
概括
本研究使用深度学习模型预测城市下水道水位,增强洪水预测和管理. 该CNN-BP模型为极端降雨事件提供准确的实时警告,改善城市排水系统运行.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 人工智能的人工智能
背景情况:
- 气候变化加剧了极端降雨,给城市排水系统带来压力,增加了洪水风险.
- 有效的洪水缓解需要优化运营和响应性灾害管理.
研究的目的:
- 开发一个实时城市排水预警系统,使用深度学习来加强洪水管理.
- 使用知识图表整合多种数据源,以全面了解洪水动态.
主要方法:
- 应用卷积神经网络与逆向传播神经网络 (CNN-BP) 结合用于预测.
- 开发了多输入多输出多步骤 (MIMOMS) 模型,用于下水道和水位预测.
- 利用知识图来整合各种数据源进行洪水动态分析.
主要成果:
- CNN-BP模型实现了高精度:下水道水位的R2为0.97,在T+1.1时的内部/外部水位的R2为0.99.
- 在强烈降雨期间捕捉水位趋势方面表现出卓越的预测准确性.
- 验证了模型的可靠实时响应能力.
结论:
- 该CNN-BP模型显著提高城市洪水预测和预警系统.
- 准确的预测可以实现优化运行和智能洪水控制实践.
- 该研究为城市排水系统的有效环境管理提供了强有力的框架.
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