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Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

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Abstract

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.

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Acknowledgments

This work was supported by the Special Fund for Agro-scientific Research in the Public Interest (No. 200903003) and Chinese National Key Technology R&D Program (No. 2012BAC17B01). We thank Zhejiang Provincial Government Hydrology Office for providing relevant data for the Changle River Watershed.

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Authors and Affiliations

  1. Department of Natural Resources, College of Environment and Natural Resources, Zhejiang University, 866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang Province, China

    Mei Liu & Jun Lu

  2. China Ministry of Education Key Lab of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou, 310058, China

    Jun Lu

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  1. Mei Liu
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  2. Jun Lu
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Correspondence to Jun Lu.

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Responsible editor: Michael Matthies

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Liu, M., Lu, J. Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?. Environ Sci Pollut Res 21, 11036–11053 (2014). https://doi.org/10.1007/s11356-014-3046-x

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  • DOI: https://doi.org/10.1007/s11356-014-3046-x

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