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Cascade Correlation Artificial Neural Networks for Estimating Missing Monthly Values of Water Quality Parameters in Rivers

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  • Maria Diamantopoulou
  • Vassilis Antonopoulos
  • Dimitris Papamichail

Abstract

Three layer cascade correlation artificial neural network (CCANN) models have been developed for the prediction of monthly values of some water quality parameters in rivers by using monthly values of other existing water quality parameters as input variables. The monthly data of some water quality parameters and discharge, for the time period 1980–1994, of Axios river, at a station near the Greek – FYROM borders and for the time period 1980–1990, of Strymon river, at a station near the Greek – Bulgarian borders, were selected for this study. The training of CCANN models was achieved by the cascade correlation algorithm which is a feed-forward and supervised algorithm. Kalman's learning rule was used to modify the artificial neural network weights. The choice of the input variables introduced to the input layer was based on the stepwise approach. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The final network arhitecture and geometry were tested to avoid over-fitting. The selected CCANN models gave very good results for both rivers and seem promising to be applicable for the estimation of missing monthly values of water quality parameters in rivers. Copyright Springer Science + Business Media, Inc. 2007

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  • Maria Diamantopoulou & Vassilis Antonopoulos & Dimitris Papamichail, 2007. "Cascade Correlation Artificial Neural Networks for Estimating Missing Monthly Values of Water Quality Parameters in Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 649-662, March.
    • Handle: RePEc:spr:waterr:v:21:y:2007:i:3:p:649-662
    DOI: 10.1007/s11269-006-9036-0
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    References listed on IDEAS

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    1. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
    2. Avinash Agarwal & R. Singh, 2004. "Runoff Modelling Through Back Propagation Artificial Neural Network With Variable Rainfall-Runoff Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(3), pages 285-300, June.
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    1. Ping-Feng Pai & Fong-Chuan Lee, 2010. "A Rough Set Based Model in Water Quality Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2405-2418, September.
    2. G. Saharidis & I. Androulakis & M. Ierapetritou, 2011. "Model building using bi-level optimization," Journal of Global Optimization, Springer, vol. 49(1), pages 49-67, January.
    3. Maya Rajnarayan Ray & Arup Kumar Sarma, 2016. "Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4695-4711, October.
    4. Maryam Zavareh & Viviana Maggioni, 2018. "Application of Rough Set Theory to Water Quality Analysis: A Case Study," Data, MDPI, vol. 3(4), pages 1-15, November.
    5. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    6. Tao Jiang & Ming Zhong & Ying-jie Cao & Long-jian Zou & Bo Lin & Ai-ping Zhu, 2016. "Simulation of Water Quality under Different Reservoir Regulation Scenarios in the Tidal River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3593-3607, August.

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