Risk Assessment of Factors Influencing Non-Revenue Water Using Bayesian Networks and Fuzzy Logic

Massoud Tabesh (), Abbas Roozbahani (), Bardia Roghani (), Niousha Rasi Faghihi () and Reza Heydarzadeh ()
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Massoud Tabesh: University of Tehran
Abbas Roozbahani: University of Tehran
Bardia Roghani: University of Tehran
Niousha Rasi Faghihi: University of Tehran
Reza Heydarzadeh: University of Tehran

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 11, No 7, 3647-3670

Abstract: Abstract This paper aims to initially, identify effective factors of Non Revenue Water (NRW) and its three major components: apparent losses, real losses and non-revenue authorized consumptions in water distribution networks. In the next step, they should be ranked according to their potential in the reduction of the amount of NRW. Besides, incidence of each NRW components imposes some special economic and social impacts. In the present study, by considering data scarcity and uncertainty of the available data and information, risk assessment of NRW and its components has been done through two stages. The first stage is initiated by designing a questionnaire so as to collect data and information about 41 identified parameters in part of Tehran Water and Wastewater Company as the research pilot. Then, in order to demonstrate the probability relationships between factors influencing NRW components, Bayesian Network (BN) is used. At the end of this stage, the parameters are prioritized in terms of their impact. Through the following stage, consequences of NRW components existence are identified and fuzzified, to consider their uncertainty. After that, based on the risk definition and Fuzzy Inference Systems )FIS( concept, fuzzified probability and consequences are combined and NRW components’ risk is calculated. In this study, the calculated risk of NRW components in the study area are “Moderately High” and equal to 7.05, 6.95 and 6.4 for apparent loss, real loss and non-revenue authorized consumptions, respectively which means that decision makers and managers of this district should take serious actions to reduce the amount of risk.

Keywords: Bayesian networks; Fuzzy logic; Non-revenue water; Risk assessment (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11269-018-2011-8

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