Dissolved oxygen concentration predictions for running waters with using hybrid machine learning techniques

Water is one of the most essential elements in nature that forms the basis of human life and contributes to the economic growth and development of societies. Safe water is closely related to environmental health and activities. The lives of all the animals on our planet depend on water and oxygen. Moreover, sufficient dissolved oxygen (DO) is crucial for the survival of aquatic animals. In the present research, temperature (T) and flow (Q) variables were used to predict DO. The time series were monthly and data were related to the Cumberland River in the southern United States from 2008 to 2018. Support vector regression (SVR) was employed for prediction of the model in both standalone and hybrid forms. The employed hybrid models consisted in SVR combined with metaheuristic algorithms of chicken swarm optimization (CSO), social ski-driver (SSD) optimization, Black widow optimization (BWO), and the Algorithm of the innovative gunner (AIG). Pearson correlation coefficient was utilized to select the best input combination. Box plots and Taylor diagrams were employed in the interpretation of the results. It was observed that all the four hybrid models achieved better results. Also according to the evaluation criteria among the models used the following were found: SVR-AIG with the coefficient of determination (R-2 = 0.963), the root mean square error (RMSE = 0.644 mg/l), the mean absolute value of error (MAE = 0.568 mg/l), the Nash-Sutcliffe coefficient (NS = 0.864), and bias percentage (BIAS = 0.001). Overall the research showed that hybrid models increased the accuracy of the single SVR model by 6.52-1.75%.

Automated summary

This study predicted dissolved oxygen in water using temperature and flow variables, achieving more accurate results with support vector regression and hybrid models. Data from the Cumberland River in the southern United States were used, and the findings showed that the hybrid models outperformed the single model in terms of accuracy.