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Accurate water level prediction is of great importance for water infrastructures such as dams, embankments, and agriculture. However, the water level has nonlinear characteristics, which make it very challenging to accurately predict the water level. This study proposes a combined model based on variational mode decomposition (VMD), a genetic algorithm–the ELMAN neural network–VMD–the autoregressive integrated moving average (ARIMA) model (GA–ELMAN–VMD–ARIMA). Firstly, VMD preprocesses the original water level and predicts each subsequence with the GA–ELMAN model. Then the error sequence is decomposed by VMD and predicted by the ARIMA model. Finally, the predicted water level is corrected. Using three groups of data from different sites, 10 models are established to compare the performance of the model. The results show that the combination of the VMD algorithm and the GA–ELMAN model can improve the performance of prediction on datasets. In addition, it also shows that the VMD double processing can greatly improve the prediction accuracy. HIGHLIGHTS The variational mode decomposition (VMD) double processing is used for water level prediction.; A genetic algorithm is used to optimize the hyperparameters of the ELMAN neural network.; The error correction method is used to improve the prediction accuracy.;