A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction.

Accurate prediction of runoff is of great significance for rational planning and management of regional water resources. However, runoff presents non-stationary characteristics that make it impossible for a single model to fully capture its intrinsic characteristics. Enhancing its precision poses a significant challenge within the area of water resources management research. Addressing this need, an ensemble deep learning model was hereby developed to forecast monthly runoff. Initially, time-varying filtered based empirical mode decomposition (TVFEMD) is utilized to decompose the original non-stationarity runoff data into intrinsic mode functions (IMFs), a series of relatively smooth components, to improve data stability. Subsequently, the complexity of each sub-component is evaluated using the permutation entropy (PE), and similar low-frequency components are clustered based on the entropy value to reduce the computational cost. Then, the temporal convolutional network (TCN) model is built for runoff prediction for each high-frequency IMFs and the reconstructed low-frequency IMF respectively. Finally, the prediction results of each sub-model are accumulated to obtain the final prediction results. In this study, the proposed model is employed to predict the monthly runoff datasets of the Fenhe River, and different comparative models are established. The results show that the Nash-Sutcliffe efficiency coefficient (NSE) value of this model is 0.99, and all the indicators are better than other models. Considering the robustness and effectiveness of the TVFEMD-PE-TCN model, the insights gained from this paper are highly relevant to the challenge of forecasting non-stationary runoff.