三峡大学学报（自然科学版）2026，Vol.48Issue(2)：1-7,7.DOI:10.13393/j.cnki.issn.1672-948X.2026.02.001
稳态数据驱动的明渠复杂输水建筑物综合水头损失预测
Steady-State Data-Driven Prediction of Integrated Head Loss in Complex Open-Channel Water Conveyance Structures
摘要
Abstract
Accurate prediction of the total head loss in water conveyance structures is a key component in the development of hydrodynamic models and serves as a fundamental basis for precise simulation of open-channel water transfer projects.In practical engineering applications,reliable prediction methods are often difficult to establish due to complex local flow patterns,limited monitoring points,and dynamic gate group operations.To address these challenges,this study proposes a steady-state data-driven approach for predicting the total head loss in complex open-channel water conveyance structures.The method employs steady-state identification techniques to extract representative datasets,which are then used to construct a neural network-based data-driven model for accurate head loss prediction.To validate its effectiveness,the method was applied to aqueducts and inverted siphons in the Middle Route of the South-to-North Water Diversion Project,where head losses were predicted using upstream water depth and flow discharge.The results show that,compared with traditional hydraulic methods,the proposed approach reduces the mean absolute error by 27%and 38%,respectively.Overall,the method demonstrates superior accuracy and applicability in head loss prediction,providing strong support for simulation and forecasting in water transfer operations.关键词
水头损失/神经网络/稳态数据/输水建筑物/明渠调水工程Key words
head loss/neural network/steady-state data/water conveyance structures/open-channel water transfer分类
建筑与水利引用本文复制引用
徐湛,张云辉,张召,王文川,顾起豪..稳态数据驱动的明渠复杂输水建筑物综合水头损失预测[J].三峡大学学报（自然科学版）,2026,48(2):1-7,7.基金项目
国家重点研发计划项目(2023YFC3209404) （2023YFC3209404）
