Abstract
The paper presents neural networks as models for prediction of the water intake. For construction of prediction models three types of neural networks were used: linear network, multi-layer network with error backpropagation and Radial Basis Function network (RBF).
The prediction models were compared for obtaining optima quality 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. Further research concerning the creation of prognosis models should be directed towards constructing models not only for particular days, but also for the complete week, four seasons of the year: spring, summer, autumn and winter, and finally the entire year.
The paper is issued in range of the project no R11 001 01 financed by the Ministry of Higher Education and Sciences.
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Rojek, I. (2008). Neural Networks as Prediction Models for Water Intake in Water Supply System. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_104
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DOI: https://doi.org/10.1007/978-3-540-69731-2_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
Online ISBN: 978-3-540-69731-2
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