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Tytuł artykułu

Kohonen Artificial Networks for the Verification of the Diameters of Water-pipes

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The design of the water distribution system is inherently linked to the execution of calculations, which aim, among other things, to determine the flow rate through individual pipes and the selection of diameters at the appropriate speed. Each step in the calculations is followed by an evaluation of the results and, if necessary, a correction of the data and further calculations. It is up to the designer to analyse the accuracy of the calculation results and is time-consuming for large systems. In this article, a diagnostic method for the results of hydraulic calculations, based on Kohonen Network, which classifies nominal diameters [DN] on the basis of data, in the form of flows, has been proposed. After calculating the new variant of the water distribution system, the individual calculation sections are assigned to the neurons of the topological map of Kohonen Network drawn up for nominal diameters. By comparing the diameter used for the calculation, with the diameter obtained on the topological map, the accuracy of the chosen diameter can be assessed. The topological map, created as a result of labelling the neurons of the output layer of the Kohonen Network, graphically shows the position of the classified diameter, relative to those diameters with similar input values. The position of a given diameter, relative to other diameters, may suggest the need to change the diameter of the pipe.
Rocznik
Tom
Strony
835--844
Opis fizyczny
Bibliogr. 12 poz., rys., tab.
Twórcy
  • Białystok Technical University, Poland
  • F.B.I. TASBUD Capital Group, Warsaw, Poland
  • Białystok Technical University, Poland
Bibliografia
  • Aksela, K., Aksela, M., & Vahala, R. (2009). Leakage detection in a real distribution network using a SOM. Urban Water Journal, 6(4), 279-289.
  • Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford: Oxford Univ. Press.
  • Blokker, E. J. M., Furnass, W. R., Machell, J., Mounce, S. R., Schaap, P. G., & Boxall, J. B. (2016). Relating Water Quality and Age in Drinking Water Distribution Systems Using Self-Organising Maps. Environments, 3(2).
  • Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM plus k-Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88.
  • Czapczuk, A., Dawidowicz, J., & Piekarski, J. (2015). Artificial Intelligence Methods in the Design and Operation of Water Supply Systems. Rocznik Ochrona Srodowiska, 17, 1527-1544.
  • Dawidowicz, J. (2012). Expert System for Evaluation of Water Distribution System Created with an Inductive Inference. Rocznik Ochrona Srodowiska, 14, 650-659.
  • Dawidowicz, J., Kruszynski, W., Andraka, D., & Czapczuk, A. (2018). Assessing the Diameters of Water Pipes Using the k-Nearest Neighbours Method in the Calculations of Water Distribution Systems. Rocznik Ochrona Srodowiska, 20, 528-537.
  • Kangas, J., Kohonen, T. (1996). Developments and applications of the self-organizing map and related algorithms. Mathematics and Computers in Simulation, 41, 3-12.
  • Kohonen, T. (2001). Self-Organizing Maps. Springer Series in Information Sciences, 30, Springer-Verlag.
  • Konar, A. (2005). Computational Intelligence: Principles, Techniques and Applications. Springer-Verlag.
  • Kutylowska, M. (2016). Prediction of Water Conduits Failure Rate – Comparison of Support Vector machine and Neural Network. Ecological Chemistry and Engineering a-Chemia i Inzynieria Ekologiczna A, 23(2), 147-160.
  • Mielcarzewicz, W.E. (2000). Calculation of water supply systems. Warszawa: Arkady (in Polish).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1aea2fae-703a-47e2-b7bd-a3687e21bdd8
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