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Estimation of Nitrogen Removal Effect in Groundwater Using Artificial Neural Network

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

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Abstract

Groundwater contamination by nitrate is a globally growing problem. Biological denitrification is a simple and cost effective method. However, this process is non-linear, complex and multivariable. This paper presents the application of artificial neural network (ANN) in denitrification process in ground water. Experimental results showed that the ANN was able to predict the output water quality parameters—including nitrate as well as nitrite and COD. Most of relative error of NO3 −-N and COD were in the range of ±10% and ±5% respectively. The ANN model of nitrate removal in ground water prediction results produced good agreement with experimental data.

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Author information

Authors and Affiliations

  1. Department of Environmental Engineering, Harbin University of Commerce, 50# box, Tongda Road 138#,Daoli district, Harbin, 150076, China

    Jinlong Zuo

Authors
  1. Jinlong Zuo
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

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© 2008 Springer-Verlag Berlin Heidelberg

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Zuo, J. (2008). Estimation of Nitrogen Removal Effect in Groundwater Using Artificial Neural Network. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_32

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  • DOI: https://doi.org/10.1007/978-3-540-87734-9_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87733-2

  • Online ISBN: 978-3-540-87734-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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