Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data

This study compares the ability of Long Short-Term Memory (LSTM) tuned with Grey Wolf Optimization (GWO) and machine learning models, artificial neural network (ANN), Adaptive-Network-based Fuzzy Inference System (ANFIS), and support vector machine (SVM) enhanced with GWO in the prediction of monthly streamflow. Precipitation, temperature, and streamflow data obtained from stations in Pakistan are used as inputs to the implemented models. The first application focused on streamflow prediction using local data and applying four different input scenarios; i) precipitation-based inputs, ii) temperature-based inputs, iii) lagged streamflow inputs, and iv) combinations of previous input scenarios. In the second application, streamflow data of one station are predicted using data from another station and similar input scenarios are considered. In the first application, the LSTM-GWO performed the best in one station, whereas the ANN-GWO produced the best predictions in another station. The mean absolute percentage error in LSTM-GWO and ANN-GWO are 17.9% and 22.63, respectively. Furthermore, it is observed that the LSTM-GWO generally offers the best accuracy in peak streamflow prediction with a maximum relative error of 113%. In the second application, the LSTM-GWO is the superior model which improves the mean absolute percentage error by 13%, 60%, and 101% compared to SVR-GWO, ANN-GWO, and ANFIS-GWO, respectively.