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.
Highlights
Non-revenue water (NRW) includes water lost from physical incidents such as pipe leaks caused by bursts in a water distribution system and water-related commercial losses stemming from illegal connections, unmetered public use and meter error [1]
To estimate the NRW ratio, including the amount of water leaks, the main parameters of water distribution systems appropriate for regional characteristics are selected, and the NRW calculation model, which was developed by statistical analysis, plays an important role in the planning and operating of district metered areas (DMAs)
The grey solid line shows the result of NRW by measurement, and the estimated NRW ratio of each DMA is shown when the number of neurons in the hidden layer is set to 10, 20 and 30, respectively
Summary
Non-revenue water (NRW) includes water lost from physical incidents such as pipe leaks caused by bursts in a water distribution system and water-related commercial losses stemming from illegal connections, unmetered public use and meter error [1]. Principal component analysis (PCA) was used to reduce the dimensionality of the original data [16,17] These statistical techniques and performance indicators were helpful in forecasting NRW, and a number of parameters of water distribution systems were proposed and analyzed. To estimate the NRW ratio, including the amount of water leaks, the main parameters of water distribution systems appropriate for regional characteristics are selected, and the NRW calculation model, which was developed by statistical analysis, plays an important role in the planning and operating of DMA. A model for NRW ratio calculation for Incheon was developed by considering an ANN and parameters of major water distribution systems. Sustainability 2017, 9, 1933 and demand using EPANET 2.0 (Environmental Protection Agency, Cincinnati, OH, USA, 2000), a hydraulic numerical analysis model for water distribution systems
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