Explainable deep learning hybrid models for enhanced prediction of river dissolved oxygen concentration

Band S.S. Qasem S.N. Mansor Z. Pai H.-T. Mehdizadeh S. Gupta B.B. Mosavi A.
January 2026 Elsevier Ltd

Journal of Water Process Engineering
2026 #81

Predicting river water quality is important in hydrological, environmental, and water resources management fields. In this context, the concentration of dissolved oxygen (DO) in rivers is one of the most important water quality indicators that needs to be forecasted accurately. This study firstly developed two deep learning (DL) models, including Bidirectional Long Short-Term Memory (BiLSTM) and Temporal Fusion Transformer Neural Network (TFTNN) for predicting daily DO concentrations at two river stations in United States (Beaverton Creek and North Umpqua River) in timeframe from 2016 to 2023. Then, a hybrid model was implemented via hybridizing TFTNN and BiLSTM to establish the TFTNN-BiLSTM model. The findings revealed that the developed TFTNN-BiLSTM performed better compared with their baseline models. Finally, a novel two-stage hybrid model named RBMO-TFTNN-BiLSTM was proposed by coupling TFTNN, BiLSTM, and an optimizer named Red-Billed Blue Magpie Optimization (RBMO). The outcomes denoted the superior performance of proposed RBMO-TFTNN-BiLSTM than TFTNN, BiLSTM, and TFTNN-BiLSTM. During test stage, root mean square error of best RBMO-TFTNN-BiLSTM relative to baseline TFTNN and BiLSTM was reduced by 51.81 % and 47.66 % (Beaverton Creek), 40.18 % and 41.48 % (North Umpqua River). A Categorical Boosting (CatBoost) was also developed for a comparison, and the results indicated its lower accuracy than DL methods. Assessing the outcomes of Shapley Additive exPlanations (SHAP) illustrated that river water temperature and pH presented the highest and least impacts on the output results of models, respectively. The proposed hybrid methodologies in this study offer reliable predictive tools for accurate prediction of river DO concentration

Artificial intelligence , Baseline models , Data science , Deep learning , Dissolved oxygen , Hybrid models , Machine learning , Prediction

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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, 11432, Saudi Arabia
Center for Software Technology and Management, Universiti Kebangsaan Malaysia, Selangor, Bangi, 43600, 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, 413, 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
School of Cybersecurity, Korea University, Seoul, South Korea
Ludovika University of Public Service, Budapest, Hungary
Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Univerzita J. Selyeho Komarom, Slovakia

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
School of Cybersecurity
Ludovika University of Public Service
Abylkas Saginov Karaganda Technical University
Univerzita J. Selyeho Komarom

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