ABSTRACT

To cope with the nonlinear and nonstationarity challenges faced by conventional runoff forecasting models and improve daily runoff prediction accuracy, a hybrid model-based “feature decomposition-component prediction-result reconstruction” named VMD-LSTM-PSO was proposed. First, variational mode decomposition (VMD) was used to decompose the original daily runoff series into a discrete number of intrinsic mode functions (IMFs) to produce clearer signals. Then, for each IMF, a long short-term memory (LSTM) network was applied to establish the prediction model, and a particle swarm optimization (PSO) algorithm was utilised to optimise the number of hidden layer nodes and the learning rate of the LSTM. The model was applied in three hydrologic stations in the main stream of the Yellow River of China and compared with the complementary ensemble empirical mode decomposition coupled long short-term memory and particle swarm optimization (CEEMD-LSTM-PSO), wavelet transform coupled long short-term memory and particle swarm optimization (WT-LSTM-PSO) and LSTM-PSO models. Based on its high predictive accuracy and stability, the novel model promises to be a preferred data-driven tool for hydrological forecasting in practice.