Neural networks as prediction models for water intake in water supply system

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. © 2008 Springer-Verlag Berlin Heidelberg.