Simulation of river flow evolution in the Yellow River main stream based on the coupling of physical mechanisms and deep learning

Evolution of river flow is vital for water regulation in the Yellow River basin. There is an urgent need for a high-precision, low-latency simulation model that considers an artificial lateral water cycle to meet the demand for accurate and refined water regulation from water sources to end-users. Based on the analysis of unbalanced water volume in the Yellow River's main stream, this study used the CNN-LSTM algorithm to build flow evolution models. The SCE-UA algorithm was used to tune the parameters. The “store-wet-release-dry” mechanism of the Longyangxia Reservoir was embedded in the globally-calibrated CNN-LSTM model for simulation. Model comparison indicated the following: ① Unbalanced water volume in the main stream is in the order of middle ＞ lower ＞ upper reaches, with no significant sub-interval correlation. ② The comprehensive evaluation indicators (E_R-R-M) mean values of the hydrological method, CNN-LSTM (local optimization), CNN-LSTM (global optimization), and the coupled simulation were 0.627, 0.613, 0.774, and 0.811 respectively. The upper and lower reaches had higher accuracy than the middle reaches. ③ Building a deep neural network guided by physical simulation can effectively limit the solution space, with 29.3% higher accuracy than the hydrological method, which has practical value for water regulation in the Yellow River.