Spring Flow Prediction Model Based on VMD and Attention Mechanism LSTM

Spring flow prediction is the basis for water resources management, allocation, and effective utilization. To improve the accuracy of spring flow prediction, a hybrid model is used to predict, which combines variational modal decomposition (VMD), long and short-term memory (LSTM) network, and attention mechanism to overcome the endpoint effect and modal confounding problems of traditional empirical modal decomposition. This study explores the performance of VMD-LSTM-Attention and VMD-LSTM, LSTM models in spring water prediction. The experimental results confirm the effectiveness of VMD-LSTM-Attention in spring water prediction. Therefore, this hybrid model is robust and superior for predicting highly non-smooth and non-linear watersheds and can provide a reference for practical hydrological prediction.