2013 Volume 7 Issue 4
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Liang Qibin, Liu Yungen, Tian Kun, Wang Wanbin. Application of RBF and Elman neural network in prediction on pollutant removal efficiency of constructed wetland with different compound substrates[J]. Chinese Journal of Environmental Engineering, 2013, 7(4): 1368-1372.
Citation: Liang Qibin, Liu Yungen, Tian Kun, Wang Wanbin. Application of RBF and Elman neural network in prediction on pollutant removal efficiency of constructed wetland with different compound substrates[J]. Chinese Journal of Environmental Engineering, 2013, 7(4): 1368-1372.
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Application of RBF and Elman neural network in prediction on pollutant removal efficiency of constructed wetland with different compound substrates

  • 1.

    College of Environmental Science and Engineering, Southwest Forestry University, Kunming 650224, China

  • 2.

    National Plateau Wetlands Research Center, Kunming 650224, China

  • Received Date: 04/06/2012
    Accepted Date: 21/04/2012
    Available Online: 09/04/2013
    Fund Project:
  • The neural network model was build to predict treatment efficiency of the constructed wetland because of the complex decontamination mechanism and nonlinear. In 4 month experiment, 56 groups of COD removal rate were obtained from constructed wetland with different compound substrates. To predict the COD removal rate, the models based on the radial basis function(RBF) and Elman neural network were presented after wavelet de-noising under the environment of Matlab. The results showed that the RMS error of RBF and Elman neural network are 0.0186 and 0.0163, respectively, which means that the precision of the model is high. The COD removal rates are 49.4%~59.0%.
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Application of RBF and Elman neural network in prediction on pollutant removal efficiency of constructed wetland with different compound substrates

  • Received Date: 04/06/2012
  • Accepted Date: 21/04/2012
  • Available Online: 09/04/2013
Fund Project:

Abstract: The neural network model was build to predict treatment efficiency of the constructed wetland because of the complex decontamination mechanism and nonlinear. In 4 month experiment, 56 groups of COD removal rate were obtained from constructed wetland with different compound substrates. To predict the COD removal rate, the models based on the radial basis function(RBF) and Elman neural network were presented after wavelet de-noising under the environment of Matlab. The results showed that the RMS error of RBF and Elman neural network are 0.0186 and 0.0163, respectively, which means that the precision of the model is high. The COD removal rates are 49.4%~59.0%.

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