Mid-long term forecasting of reservoir inflow using the coupling of time-varying filter-based empirical mode decomposition and gated recurrent unit.

Accurate and reliable runoff forecast is beneficial to watershed planning and management and scientific operation of water resources system. However, due to the comprehensive influence of climatic conditions, geographical environment, and human activities, the runoff series is nonlinear and non-stationary, and there are still great challenges in mid-long term runoff forecasting. In order to improve the prediction accuracy, a novel model TVF-EMD-PE-PSO-GRU (TEPPG) was proposed in this study. Firstly, several intrinsic mode functions (IMFs) were obtained by decomposing the original runoff series by time-varying filter-based empirical mode decomposition (TVF-EMD). Secondly, the permutation entropy (PE) algorithm was used to calculate the complexity of each IMF, and the IMF with similar complexity was combined. Then, the gated recurrent unit (GRU) model based on particle swarm optimization (PSO) was used to predict each IMF after merging. Finally, the prediction results of each IMF were superimposed to obtain the final results. And compared with three models such as TVF-EMD-PSO-GRU, extreme-point symmetric mode decomposition coupled gated recurrent unit and particle swarm optimization (ESMD-PSO-GRU), complete ensemble empirical mode decomposition with adaptive noise coupled gated recurrent unit, and particle swarm optimization (CEEMDAN-PSO-GRU). The monthly and annual runoff forecasting of Tangnaihai hydrological station in the upper reaches of the Yellow River and Cuntan hydrological station in the upstream of the Yangtze River was taken as examples to test the performance of the model. The results show that, compared with the other three models, the TEPPG model had the highest prediction accuracy and was relatively stable in both monthly and annual runoff forecasts. Thus, the proposed method was developed to support the decision-making of water resource system.