TY  - EJOU
AU  - Ikram, Rana Muhammad Adnan 
AU  - Han, Jing-Cheng 
AU  - Ewees, Ahmed A. 
AU  - Wang, Mo 
AU  - Kisi, Ozgur 
AU  - Heddam, Salim 
AU  - Zounemat-Kermani, Mohammad 

TI  - Innovative Deep Learning Models for Streamflow Forecasting in High Elevation Catchments
T2  - Computer Modeling in Engineering \& Sciences

PY  - 
VL  - 
IS  - 
SN  - 1526-1506

AB  - Two-phase optimized machine learning and deep learning models play a key role in enhancing the prediction accuracy of nonlinear time series modeling. This study assesses the performance of a novel two-phase optimized Long Short-Term Memory (LSTM) model with integration of Aquila Optimizer (AO) and Wild Horse Optimizer (WHO) in predicting monthly streamflow in a snow-fed catchment. The two-phase optimized LSTM-WHOAO model is compared with single-phase optimized models such as LSTM-GA (Genetic Algorithm), LSTM-GWO (Grey Wolf Optimizer), LSTM-WOA (Whale Optimization Algorithm), LSTM-AO, and LSTM-WHO. The outcomes acquired from the deep learning models were compared using four statistical measures: root-mean-square-error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R<sup>2</sup>). The LSTM-WHOAO model exhibited the best performance during training, with a mean RMSE of 51.930 and an R<sup>2</sup> of 0.851. The LSTM-WHO model also demonstrated robust performance, achieving an average RMSE of 53.900 and an R<sup>2</sup> value of 0.840. Other models, such as LSTM-AO and LSTM-WOA, showed average RMSE of 57.135 and 58.978, respectively, indicating improved performance over the single LSTM model. For the testing stage, the LSTM-WHOAO model remained more effective than other models, with an average RMSE of 70.413 and an R<sup>2</sup> of 0.755. For peak streamflow events, the LSTM-WHOAO model had the lowest absolute error (201.4%), significantly reducing prediction error compared to other models such as LSTM-GA (333.9%) and LSTM (347.1%). For peak streamflow events, the LSTM-WHOAO model had the lowest absolute error (201.4%), significantly reducing the prediction error compared to other models such as LSTM-GA (333.9%) and LSTM (347.1%). Models that incorporated snow-covered area (SCA) data, such as LSTM-WHOAO and LSTM-WHO, showed lower RMSE and higher R<sup>2</sup> values, underscoring the importance of considering snow cover dynamics in streamflow forecasting. The LSTM-WHOAO model proved to be the most successful, showing superior results in both the training and testing stages, as well as in peak streamflow predictions. By addressing the unique challenges of snow-fed catchments, this research offers valuable insights, especially into the application of advanced ML techniques in hydrology.
KW  - Streamflow prediction; long short-term memory; genetic algorithm; grey wolf optimizer; whale optimization algorithm; aquila optimizer; wild horse optimizer algorithm; snow covered area

DO  - 10.32604/cmes.2026.083313