Hybrid machine learning and deep learning models for river suspended sediment load forecasting

S. Band S. Qasem S.N. Mansor Z. Pai H.-T. Mehdizadeh S. Gupta B.B. Mosavi A.
2026 Taylor and Francis Ltd.

Engineering Applications of Computational Fluid Mechanics
2026 #20 Issue 1

Modeling the suspended sediment load (SSL) in rivers is of great importance in various fields, e.g. hydrological sciences, water resources management, and dams engineering. In this study, the daily SSL time series at two distinct stations are modeled. The machine learning methods of random forest (RF) and long short-term memory (LSTM) are implemented. The outcomes state that the RF yielded better SSL predictions compared with LSTM. Besides the aforementioned individual models, this study improves the SSL forecasts through the development of hybrid versions of RF and LSTM. To achieve this, an optimizer, i.e. the immune system algorithm (ISA), was initially coupled to the RF. Next, recurrent neural networks (RNN) were hybridized with an LSTM. Therefore, the hybrid forms of RF and LSTM, i.e. RF-ISA and RNN-LSTM, were proposed. The findings reveal that both hybrid models, specifically RNN-LSTM, outperformed their relevant individual forms. The RF-ISA model, with its optimized hyperparameter selection, demonstrated better generalization compared to the baseline RF model, while the RNN-LSTM model effectively captured temporal trends in SSL fluctuations. The values of evaluation error metrics, namely root mean square error (RMSE), normalized RMSE (NRMSE), correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), Willmott’s index (WI), and percent bias (PBIAS) in the test phase for the best model of RNN-LSTM were achieved as follows: R = 0.9434, RMSE = 105567 ton/day, NRMSE = 0.0291, NSE = 0.8893, WI = 0.9701, PBIAS = −0.7135% (first station), and R = 0.9824, RMSE = 57238 ton/day, NRMSE = 0.0311, NSE = 0.9632, WI = 0.9912, PBIAS = 0.0271% (second station). The outcomes of SHapley Additive exPlanations (SHAP) explainer exhibited that one-day delayed SSL and river discharge data represented the greatest and least impacts on the models output, respectively.

artificial intelligence , deep learning , forecasting , immune system algorithm , long short-term memory , machine learning , random forest , recurrent neural networks , Suspended sediment load

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International Graduate School of AI, National Yunlin University of Science and Technology, Douliu, Taiwan
Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Center for Software Technology and Management, Universiti Kebangsaan Malaysia, Bangi, Malaysia
Department of Big Data Business Analytics, National Pingtung University, Pingtung, Taiwan
Water Engineering Department, Urmia University, Urmia, Iran
Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India
John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
Ludovika University of Public Service, Budapest, Hungary
Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Univerzita J. Selyeho, Komarno, Slovakia

International Graduate School of AI
Department of Information Management
Computer Science Department
Center for Software Technology and Management
Department of Big Data Business Analytics
Water Engineering Department
Department of Computer Science and Information Engineering
Department of Medical Research
Symbiosis Centre for Information Technology (SCIT)
John von Neumann Faculty of Informatics
Ludovika University of Public Service
Abylkas Saginov Karaganda Technical University
Univerzita J. Selyeho

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