Comparison of MLP and RBF Neural Networks in the Task of Classifying the Diameters of Water Pipes

Abstract

Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.

Analysis of the Use of a Low-Power Photovoltaic System to Power a Water Pumping Station in a Tourist Town

“…Water losses can be reduced with regard to real losses, where we reduce the number of failures or the duration of a single failure, or apparent losses, where we introduce more accurate water meters and remote reading systems [13]. Reducing energy losses involves, among other things, the appropriate selection of pipe diameters, e.g., the use of computer methods and artificial neural networks [14,15].…”