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Estimation of Non-Revenue Water Ratio for Sustainable Management Using Artificial Neural Network and Z-Score in Incheon, Republic of Korea

Author

Listed:
  • Dongwoo Jang

    (Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea)

  • Gyewoon Choi

    (Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea)

Abstract

The non-revenue water (NRW) ratio in a water distribution system is the ratio of the loss due to unbilled authorized consumption, apparent losses and real losses to the overall system input volume (SIV). The method of estimating the NRW ratio by measurement might not work in an area with no district metered areas (DMAs) or with unclear administrative district. Through multiple regression analyses is a statistical analysis method for calculating the NRW ratio using the main parameters of the water distribution system, although its disadvantage is lower accuracy than that of the measured NRW ratio. In this study, an artificial neural network (ANN) was used to estimate the NRW ratio. The results of the study proved that the accuracy of NRW ratio calculated by the ANN model was higher than by multiple regression analysis. The developed ANN model was shown to have an accuracy that varies depending on the number of neurons in the hidden layer. Therefore, when using the ANN model, the optimal number of neurons must be determined. In addition, the accuracy of the outlier removal condition was higher than that of the original data used condition.

Suggested Citation

  • Dongwoo Jang & Gyewoon Choi, 2017. "Estimation of Non-Revenue Water Ratio for Sustainable Management Using Artificial Neural Network and Z-Score in Incheon, Republic of Korea," Sustainability, MDPI, vol. 9(11), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:1933-:d:116378
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    References listed on IDEAS

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