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澄清城市洪水应对特征,并通过稀疏的数据改善可解释的洪水预测,考虑到降雨和排水管道沉的合效应
Guangxin Liu1, Hongyuan Fang2, Danyang Di1
1School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, Henan, PR China.
The Science of the total environment
|September 11, 2024
概括
城市洪水因降雨和污泥管道而恶化. 一个新的合模型和智能预测系统 (Bayes-CNN-BLSTM) 准确地模拟和预测城市积水,考虑这些综合效应.
科学领域:
- 环境科学环境科学
- 水文学的水文学
- 城市规划是城市规划.
背景情况:
- 城市洪水是一个日益严重的全球问题,由气候变化和城市化加剧.
- 现有的模型往往忽略了降雨强度和排水管道沉对洪水动态的综合影响.
- 这项研究解决了对这些关键因素考虑的综合模型的需求.
研究的目的:
- 开发和验证用于城市洪水模拟的合地表-地下水文模型.
- 在降雨和管道沉的联合影响下分析城市洪水应对特征.
- 建立一个智能预测模型,以提高积水预测的准确性.
主要方法:
- 开发了一个表面-地表合模型,集成了圣维南特,曼宁,SWMM (1D管道) 和LISFLOOD-FP (2D表面) 方程.
- 结合模型分析了城市洪水对降雨和管道沉的反应.
- 使用卷积神经网络 (CNN),双向长期短期记忆 (BLSTM) 和贝叶斯优化 (Bayes) 创建了一个智能预测模型 (Bayes-CNN-BLSTM).
主要成果:
- 与初始条件 (36.67%的过载,3.05%的溢出) 相比,沉管道显著增加了管道过载率 (高达68.37%) 和溢出节点 (高达25.89%).
- 增加的联合沉系数 (CSC) 导致了更大的淹水面积和水量.
- 贝叶斯-CNN-BLSTM模型在预测各种降雨情景中的城市洪水方面表现出卓越的准确性,可靠性和稳定性.
结论:
- 管道沉严重影响城市排水系统在极端降雨期间的性能.
- 开发的合模型有效地模拟了复杂的城市洪水动态.
- 智能贝叶斯-CNN-BLSTM模型提供了一个强大的解决方案,用于准确可靠的城市淹水预测.
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