Application of artificial neural network on water quality inversion in Cihu Lake
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Abstract:
The management of urban shallow lakes plays an important role in the urban ecological civilization construction. Using the IKONOS remote sensing image, two artificial neural network models, based on the BP (Back Propagation) and RBF (Radical Basis Function), were set up to inverse the COD, NH3-N, TN and TP quality conditions of the Cihu Lake. The proposed models were also compared with the multivariate linear regression model. The results indicate that the model efficiency of the two ANN models are significantly higher than the multiple linear regression model. The BP model fits the observed data best in the simulation of the NH3-N, TP, while the RBF neural network shows advantages in the simulation of the COD and TN.
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