Streamflow prediction using Long Short-Term Memory networks: a case study at the Kratie Hydrological Station, Mekong River Basin

Accurate streamflow prediction is vital for hydropower operations, agricultural planning, and water resource management. This study assesses the effectiveness of Long Short-Term Memory (LSTM) networks in daily streamflow prediction at the Kratie station, investigate different network structures and hyperparameters to optimize predictive accuracy while considering computational efficiency. Our findings underscore the significance of LSTM models in addressing streamflow prediction challenges. Training LSTM on historical streamflow data reveals the significance of the training dataset size; spanning 2013-2022 yields optimal results. Incorporating a hidden layer with a nonlinear activation function, and adding a fully connected layer improve prediction ability. However, increasing the number of neurons and layers introduces complexity and computational overhead. Careful parameter tuning, including epochs, dropout, and the number of LSTM units, is crucial for optimal performance without sacrificing efficiency. The stacked LSTM with sigmoid activation demonstrates exceptional performance, boasting a high Nash-Sutcliffe Efficiency of 0.95 and a low relative root mean square error (rRMSE) of approximately 0.002%. Moreover, the model excels in forecasting streamflow for 5-15 antecedent days, with 5 days exhibiting particularly high accuracy. These findings offer valuable insights into LSTM networks for streamflow prediction for water management in the Vietnam Mekong Delta.