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Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

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Abstract

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2, and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

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Acknowledgements

This work was supported by the Science and Technology Department of Zhejiang Province (2008C03009). The authors would like to thank the Wenzhou Environmental Protection Bureau (WEPB) for the data provided for the Wen-Rui Tang River.

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Authors and Affiliations

  1. Zhejiang Province Key Laboratory of Watershed Science and Health, Southern Zhejiang Water Research Institute (iWATER), Wenzhou Medical University, Wenzhou, 325035, China

    Xiaoliang Ji, Xu Shang, Randy A. Dahlgren & Minghua Zhang

  2. Department of Land, Air and Water Resources, University of California, Davis, CA, 95616, USA

    Randy A. Dahlgren & Minghua Zhang

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  1. Xiaoliang Ji
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  2. Xu Shang
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  3. Randy A. Dahlgren
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  4. Minghua Zhang
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Correspondence to Minghua Zhang.

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This study was funded by the Science and Technology Department of Zhejiang Province (grant number 2008C03009).

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Responsible editor: Marcus Schulz

Appendix

Appendix

Table 4 DO concentration (mg/L) in previous studies
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Fig. 13
figure 13

SVM model efficiency with different numbers of folds in cross validation

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Ji, X., Shang, X., Dahlgren, R.A. et al. Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China. Environ Sci Pollut Res 24, 16062–16076 (2017). https://doi.org/10.1007/s11356-017-9243-7

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