Comparison of Two Types of Artificial Neural Networks for Predicting Failure Frequency of Water Conduits

Abstract

This paper presents the results of a comparison between two artificial neural network structures, i.e. the multilayer perceptron and the ANN with radial basis functions, with regard to the prediction of the failure intensity (failure rate) indicator for water mains, distribution pipes and house connections. The artificial neural network architecture included seven input signals (the number of house connections, the length of water mains, distribution pipes and house connections and the number of their failures). There were three neurons (the failure frequency indicators for the three types of conduits) at the ANN’s output. Operating data from the years 1999-2013 were used to train the ANNs while the optimal model was verified using data from the year 2014. Two models (MLP 7-14-3 and RBF 7-4-3), characterized by the best agreement between the predicted results and the experimental ones, were selected from a few tens of models. The RBF ANNs would generate results showing poorer agreement with the experimental failure frequency indicator values.