Artificial neural network system analysis and Kriging methodology for estimation of non-revenue water ratio

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Şişman E., Kizilöz B.

Water Science and Technology: Water Supply, vol.20, no.5, pp.1871-1883, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 5
  • Publication Date: 2020
  • Doi Number: 10.2166/ws.2020.095
  • Journal Name: Water Science and Technology: Water Supply
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Geobase, ICONDA Bibliographic, Pollution Abstracts, Veterinary Science Database
  • Page Numbers: pp.1871-1883
  • Keywords: ANNs, DMA, Kriging, NRW ratio, water distribution systems
  • Istanbul Medipol University Affiliated: Yes


The non-revenue water (NRW) ratio parameter is significantly important for performance evaluation of water distribution systems. In order to evaluate the NRW ratio, the variables influencing this parameter should be determined. Therefore, the first aim of the paper is to define the variables which are influential on the estimation of the NRW ratio and then analyze these variables by using artificial neural networks (ANNs) methodology by means of 50 models with one, two, three, and four-variable input. Secondly, in this study, the NRW ratios have been predicted for the first time by using the Kriging methodology through only two variables. By using the data measured in 12 district meter areas (DMA) in Kocaeli, 60 models in total have been established for NRW ratio prediction through the ANN and Kriging methodologies. The ANN models are closed-box models and therefore the interpretation of the ANN model results requires higher expert opinion. As a consequence, the results show that Kriging model graphs produce much more useful information than ANN models in terms of application and interpretation.