Neural network approach for availability indicator prediction

Abstract

The principal aim of this research was to find out if artificial neural networks could be employed to predict the availability factor for water mains, distribution pipes and house connections. Modelling by means of artificial neural networks (ANNs) was carried out using the Statistica 10.0 software package. Operating data from the years 1999–2005 were used to train the ANNs while data from the next seven years of operation were used to verify the model. The optimal model (characterized by the lowest mean-square error) contained 11 hidden neurons activated by the exponential function. The linear function was used to activate the 3 output neurons. 185 training epochs sufficed to train the ANN, using the quasi-Newton method. The correlation between the availability indicator experimental values and the modelling results would remain high, amounting during model verification to R2 = 0.740, R2 = 0.823, R2 = 0.992 for respectively water mains, distribution pipes and house connections. As the availability indicator prediction example shows, the artificial neural networks are a promising tool enabling quick and easy analysis of failure frequency. It is possible to train the ANN further and change the number of training epochs and hidden neurons as well as the activation functions and training methods.