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【目的】为解决传统内涝模型因数据缺失、精度不足导致的预测不准、难以真正应用的难题,研究了一种数据模型与水力仿真模型耦合的方法。【方法】以九江市中心城区两河区域为研究区域,利用数据模型对边界条件进行数据清洗重构和数据校正,弥补实测数据的缺陷,为水力仿真模型的运行提供数据支持;同时利用水力仿真模型对各种可能的工况进行计算,为内涝预测的数据模型补充训练基础数据,以提高和验证其精度。【结果】结果显示:经过数据模型优化后的水力仿真模拟结果的平均NSE均有所提高,最高涨幅约20%,SVM机器学习模型可以较好地反映出设计降雨工况下积水的淹没范围,预测的最大积水深度与水力仿真模型模拟结果相近, RMSE约为0.007。【结论】将数据模型与水力仿真模型耦合构建新型城市内涝模型,数据模型能够完善机理模型运行的边界条件,水力仿真模型能够提升数据模型的适用性,两者相互耦合可以达到较好的应用效果。
Abstract：[Objective]To solve the problem of inaccurate prediction and difficulty in real application of traditional waterlogging models due to data loss and insufficient accuracy, a method of coupling data model with hydraulic simulation model is studied.[Methods]Taking the Lianghe area in the central urban area of Jiujiang as the research area, data model for data cleaning,reconstruction and correction of boundary conditions is used to compensate for the shortcomings of measured data and provide data support for the operation of hydraulic simulation model. At the same time, hydraulic simulation models are used to calculate various possible working conditions and supplement training basic data for data mining models of waterlogging pridiction, in order to improve and verify their accuracy. [Results]The results show that after optimizing the data model, the average NSE of the hydraulic simulation result is improved, and the highest increase is about 20%. The SVM machine learning model can reflect the inundation range of the designed rainfall conditions, and the predicted maximum water depth is close to that simulated by the hydraulic simulation model. The RMSE is close to 0. 007. [Conclusion]Coupling data model with hydraulic simulation model to construct a new urban waterlogging model, data model can improve the boundary conditions for the operation of the mechanism model, and hydraulic simulation model can enhance the applicability of data model. The mutual coupling of the two models can achieve good application effects.
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基本信息:
DOI：10.13928/j.cnki.wrahe.2026.01.006
中图分类号:TU992
引用信息:
[1]张坤林,徐佳颖,郭旻睿,等.数据模型与水力仿真模型耦合的城市内涝分析技术研究[J].水利水电技术(中英文),2026,57(01):68-89.DOI:10.13928/j.cnki.wrahe.2026.01.006.
基金信息:
上海勘测设计研究院有限公司项目(2022QT(83)-069); 国家重点研发计划项目“排水管网工程建设问题智慧管控系统研发”(2021YFC3200700)
2025-03-17
2025-03-17
2025-03-17