Flow resistance calculation in lower Yellow River based on deep learning

Abstract

[Objective] Accurate calculation of flow resistance in alluvial rivers is of great significance for river regulation and flood control engineering. Conventional resistance formulas and existing machine learning method still have multiple limitations.To improve the performance and generalization ability of resistance models, a flow resistance estimation method based on deep learning is proposed. [Methods] Hydrological features, including Froude number, volumetric sediment concentration, width-todepth ratio, diameter-to-depth ratio, annual runoff, and annual sediment load, were selected as model inputs, and a flow resistance calculation model based on deep forest was established. The model was trained and tested using measured data from hydrological stations in the lower Yellow River, and comprehensively evaluated in terms of spatiotemporal generalization ability and transfer learning performance. [Results] The model achieved a Nash-Sutcliffe efficiency(NSE) of 0. 785, a mean absolute error(MAE) of 0. 002, a root mean square error(RMSE) of 0. 003, and a mean absolute percentage error(MAPE) of 14. 618% on the test dataset. After the incorporation of spatiotemporal average features, the NSE of the model increased from 0. 681 4 to 0. 742 7, the MAE decreased from 0. 002 3 to 0. 002 1, the RMSE dropped from 0. 003 2 to 0. 002 8, and the MAPE reduced from 14. 978% to 13. 689%. When the model was transferred to completely new water-sediment conditions, the maximum decline in NSE reached 65. 35%, and the maximum increases in MAE, RMSE, and MAPE were 100%, 150%, and 123. 98%, respectively. [Conclusion] Compared with traditional resistance formulas and machine learning method, the deep forest model demonstrates superior accuracy in predicting flow resistance under general conditions in alluvial rivers. By introducing large-scale spatiotemporal average features, the model's calculation accuracy across different hydrological stations and hydrological periods is effectively improved, and its generalization ability is significantly enhanced. However, under special water-sediment conditions, the model still shows performance fluctuations. In certain cases, its calculation accuracy is even lower than that of physically based roughness formulas. Therefore, it is urgent to incorporate physical mechanisms to enhance its transfer learning capability. When addressing the calculation of movable bed resistance in the Yellow River under complex environments, emphasis should be placed on mutual verification with reliable traditional formulas.

Keywords

lower Yellow River / flow resistance / deep forest / transfer learning / Froude number / runoff / sediment transport volume / machine learning models