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This paper presents the results of failure rate prediction by means of support vector machines (SVM) – a non-parametric regression method. A hyperplane is used to divide the whole area in such a way that objects of different affiliation are separated from one another. The number of support vectors determines the complexity of the relations between dependent and independent variables. The calculations were performed using Statistical 12.0. Operational data for one selected zone of the water supply system for the period 2008–2014 were used for forecasting. The whole data set (in which data on distribution pipes were distinguished from those on house connections) for the years 2008–2014 was randomly divided into two subsets: a training subset – 75% (5 years) and a testing subset – 25% (2 years). Dependent variables (λr for the distribution pipes and λp for the house connections) were forecast using independent variables (the total length – Lr and Lp and number of failures – Nr and Np of the distribution pipes and the house connections, respectively). Four kinds of kernel functions (linear, polynomial, sigmoidal and radial basis functions) were applied. The SVM model based on the linear kernel function was found to be optimal for predicting the failure rate of each kind of water conduit. This model's maximum relative error of predicting failure rates λr and λp during the testing stage amounted to about 4% and 14%, respectively. The average experimental failure rates in the whole analysed period amounted to 0.18, 0.44, 0.17 and 0.24 fail./(km·year) for the distribution pipes, the house connections and the distribution pipes made of respectively PVC and cast iron.

The paper describes the reliability of selected water-pipe networks in Polish medium-sized cities X and Z. The IntroductionA water supply network is an essential part of the buried infrastructure. Water of proper quality should be delivered to consumers under the required pressure and in the required amount. In order to achieve these goals it is necessary to continuously monitor the technical condition of the water pipes. Proper maintenance and operation are critical for the reliability and safety of the water-pipe network. The control of the pressure inside the pipes is one of the measures leading to a decrease in the number of failures and in the unreliability of the whole system [4]. Another performance optimization measure is mathematical modelling and forecasting, which use typical models, artificial intelligence and some reliability indicators. Thanks to modelling one can relatively quickly assess the condition of water pipes [20]. According to many authors [3,15,17], the modelling of reliability indicators and technical condition and then using the modelling results by water utilities can lead to an increase in water supply network efficiency and in water quality. Also an improvement in the management of the system can be achieved in this way. Moreover, through modelling one can correctly estimate the costs of water pipe reconstruction [2,21].