稳态数据驱动的明渠复杂输水建筑物综合水头损失预测OA

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.