Farid Hassanbaki Garabaghi Semra Benzer Recep Benzer Department of Environmental Sciences, Graduate School of Natural and Applied Sciences, Gazi University 06500, Ankara, Turkey Department of Science Education, Gazi Faculty of Education, Gazi University 06500, Ankara, Turkey Department of Management Information Systems, School of Administrative and Social Sciences, Ankara Medipol University 06050, Ankara, Turkey Computational Ecology and Software ISSN 2220-721X http://www.iaees.org/publications/journals/ces/online-version.asp 2025 15 4 157 175 International Academy of Ecology and Environmental Sciences Hong Kong 29 March 2025 5 May 2025 1 December 2025 water quality dissolved oxygen environmental management deep learning Bayesian optimisation gaussian process Dissolved Oxygen (DO) serves as a crucial measure of water quality, imperative for both aquatic life and human consumption. The application of deep learning, particularly through data-driven predictions, offers a robust tool for estimating DO concentrations. Enhanced precision is achieved by fine-tuning hyperparameters. Bayesian optimization methods are amongst those noteworthy for their effectiveness. This study focuses on predicting DO levels using a Deep Neural Network model. The study uses Bayesian optimization to refine hyperparameters for the best model setup, comparing the results with a baseline model using default settings. Results indicate that the Bayesian-optimized model outperforms the baseline. The findings underscore the pivotal role of Bayesian optimization in elevating model performance, exhibiting robust generalization capabilities while significantly reducing the need for manual parameter tuning. This successful application underscores a substantial methodological advancement in environmental management, particularly in predictive modelling for indicators of aquatic ecosystem health. http://www.iaees.org/publications/journals/ces/articles/2025-15(4)/sequential-GP-UCB-Bayesian-optimization.pdf