A multi-source data-driven model of lake water level based on variational modal decomposition and external factors with optimized bi-directional long short-term memory neural network

Abstract

An accurate prediction of lake water levels is of great significance to water resource regulation, flood prevention and mitigation. However, water level fluctuations have been increasingly serious due to abnormal climate and extreme events. In view of this, a VMD-EF-OBILSTM model was constructed for lake water levels based on multiple sources of hydrological and meteorological variables. In this model, water level data are transformed into low-frequency internal and high-frequency external terms by variable modal decomposition (VMD), and they are combined with external factors (EF) for multivariate prediction. The optimized bi-directional long short-term memory (OBILSTM) invokes the attention mechanism and optimizes the model's hyperparameters by whale optimization algorithm (WOA). Ultimately, the predictions of each component are linearly combined to obtain the forecast values. The empirical results with water level data from Poyang Lake in China show that the multi-source deep learning model can achieve higher prediction accuracy and lower prediction uncertainty.