Hybrid Neural Networks as Prediction Models

  • Izabela Rojek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)


The paper presents hybrid neural networks as prediction models for water intake in water supply system. Previous research concerned establishing prediction models in the form of single neural networks: linear network (L), multi-layer network with error back propagation (MLP) and Radial Basis Function network (RBF). Currently, the models in the form of hybrid neural networks (L-MLP, L-RBF, MLP-RBF and L-MLP-RBF) were created. The prediction models were compared for obtaining optimal prognosis. Prediction models were done for working days, Saturdays and Sundays. The research was done for selected nodes of water supply system: detached house node and nodes for 4 hydrophore stations from different pressure areas of water supply system. Models for Sundays were presented in detail.


prediction model hybrid neural network water supply system 


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  1. 1.
    Studzinski, J., Bogdan, L.: Computer Aided Decisions Making System for Management, Control and Planning Water and Wastewater Systems. In: Applications of Informatics in Science, Engineering and Management, System Research Institute, Polish Academy of Sciences, Warsaw. Systems Research, vol. 49, pp. 149–157 (2006)Google Scholar
  2. 2.
    Michalski, R.S., Bratko, I., Kubat, M.: Machine Learning and Data Mining. John Wiley & Sons, Chichester (1998)Google Scholar
  3. 3.
    Smaoui, N.: A Hybrid Neural Network Model for the Dynamics of the Kuramoto-Sivashinsky Equation. In: Mathematical Problems in Engineering, vol. 3, pp. 305–321. Hindawi Publishing Corporation (2004)Google Scholar
  4. 4.
    Caciotta, M., Giarnetti, S., Leccese, F.: Hybrid Neural Network System for Electric Load Forecasting of Telecomunication Station. In: XIX IMEKO World Congress Fundamental and Applied Metrology, Lisbon, pp. 657–661 (2009)Google Scholar
  5. 5.
    Tsai, C.-F., McGarry, K., Tait, J.: Image Classification Using Hybrid Neural Networks. In: ACM Conference on Research and Development in Information Retrieval, New York, pp. 431–432 (2003)Google Scholar
  6. 6.
    Chen, H., Grant-Muller, S., Mussone, L., Montgomery, F.: A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting. Journal Neural Computing and Applications 10(3), 277–286 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Krawiec, K., Stefanowski, J.: Machine Learning and Neural Networks. Publishing House of Poznan University of Technology, Poznan (2004) (in Polish)Google Scholar
  8. 8.
    Rojek, I.: Neural Networks as Prediction Models for Water Intake in Water Supply System. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 1109–1119. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Tadeusiewicz, R., Lula, P.: Statistica Neural Networks 4.0 PL: Introduction to neural networks. StatSoft Polska, Cracow (2001) (in Polish)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Izabela Rojek
    • 1
  1. 1.Institute of Mechanics and Applied Computer ScienceKazimierz Wielki University in BydgoszczBydgoszczPoland

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