Abstract
Oxygen is crucial to keep the life cycle balance in any aspect. Aquatic life is highly influenced by the levels of dissolved oxygen (DO). This calls for not just constant monitoring of the DO in aquatic systems, but to generate an accurate prediction model for future levels of the DO. This study aims to propose an accurate prediction model for DO concentrations. The performance of the Random Forest (RF) and multilayer perceptron (MLP) algorithms was evaluated in generating the regression models. Moreover, the effect of dimensionality reduction of the data by the wrapper feature Selection method on the performance of the models was evaluated. The results showed that the RF regressor excelled MLP in performance with both the dataset of all variables and the dataset of reduced variables with the best performance achieved by the RF regressor by considering Pearson correlation coefficient (0.8052), Mean absolute error (0.8911), and root mean square error (1.2805) when trained by the dataset of reduced variables. As for the accuracy of the models, the estimation error deviation of both models declined significantly when trained by the reduced variables. When the accuracy of the prediction was increased by 0.95% by the RF regressor, the accuracy of the MLP was incremented by 5.7% when trained by the dataset of reduced variables. The results demonstrated the positive impact of the dimensionality reduction on the accuracy of both models. However, RF can be considered a robust regressor in predicting DO concentrations.
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Data availability
The data that support the findings of this study are available from the General Directorate of State Hydraulic Works, Department of Survey, Planning and Allocations, Ankara, Turkey. But restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of General Directorate of State Hydraulic Works, Department of Survey, Planning and Allocations.
Code availability
Codes which were developed in Python are available upon reasonable request.
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Acknowledgements
Special thanks are expressed to the General Directorate of State Hydraulic Works, Department of Survey, Planning and Allocations for the provision of the water quality data of Büyük Menderes Basin and all the supports thereafter.
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Formal analysis, visualization, investigation, and Software were performed by Farid Hassanbaki Garabaghi. Conceptualization and methodology were performed by Semra Benzer and Recep Benzer. The first draft of the manuscript was written by Farid Hassanbaki Garabaghi. Semra Benzer and Recep Benzer reviewed, commented and edited the previous versions of the manuscript.
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Garabaghi, F.H., Benzer, S. & Benzer, R. Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach. Environ Monit Assess 195, 879 (2023). https://doi.org/10.1007/s10661-023-11492-3
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DOI: https://doi.org/10.1007/s10661-023-11492-3