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Innovative Deep Learning Models for Streamflow Forecasting in High Elevation Catchments 认领 引用
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作者 Rana Muhammad Adnan Ikram Jing-Cheng Han +4 位作者 Ahmed A.Ewees Mo Wang Ozgur Kisi Salim Heddam Mohammad Zounemat-Kermani 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期911-934,共24页
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 L... 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(R²).The LSTM-WHOAO model exhibited the best performance during training,with a mean RMSE of 51.930 and an R² of 0.851.The LSTM-WHO model also demonstrated robust performance,achieving an average RMSE of 53.900 and an R² 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 R²of 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 R²values,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. 展开更多
关键词 Streamflow prediction long short-term memory genetic algorithm grey wolf optimizer whale optimization algorithm aquila optimizer wild horse optimizer algorithm snow covered area
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