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

Jang, Dongwoo; Choi, Gye-Woon

doi:10.3390/su9111933

Cited by 20 publications

(12 citation statements)

References 14 publications

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“…The Z-score can be used when performing analysis to distinguish the differences and the distributions of variables (Jang & Choi 2017). The variable values, x, can be standardized by taking the mean difference of each variable value and dividing it into the standard deviation.…”

Section: Data Standardization Via the Z-scorementioning

confidence: 99%

Prediction model for the leakage rate in a water distribution system

Kizilöz

2021

Water Supply

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Leakages cause real losses in water distribution systems (WDSs) from transmission lines, storage tanks, networks, and service connections. In particular, the amount of leakage increases in aging networks due to pressure effects, resulting in severe water losses. In this study, various artificial neural network (ANN) models are considered for determining monthly leakage rates and the variables that affect leakage. The monthly data, which are standardized by Z-score for the years 2016–2019, are used in these models by selecting four independent variables that affect the leakage rate regarding district metered areas and pressure metered areas in WDSs. The pressure effects are taken into consideration directly as input. The model accuracy is determined by comparing the predicted and measured data. Furthermore, the leakage rates are estimated by directly modelling the actual data with ANNs. Consequently, it is found that the model results after data standardization are somewhat better than the original nonstandardized data model results when 30 neurons are used in a single hidden layer. The reason for the higher accuracy in the standardized case compared with previous modelling studies is that the pressure effect is taken into consideration. The suggested models improve the model accuracy, and hence, the methodology of this paper supports an improved pressure management system and leakage reduction.

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Section: Data Standardization Via the Z-scorementioning

confidence: 99%

Prediction model for the leakage rate in a water distribution system

Kizilöz

2021

Water Supply

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“…There are various studies of estimating NRW using ANN was performed by . It is proved that ANN show better results than MRA in NRW estimation (Jang & Choi, 2017, Jang 2017). In particular, Jang (2017Jang ( , 2018 and Jang et al (2018) suggested that the combination of PCA and ANN is the optimal method for estimating NRW using statistical methods.…”

Section: Introductionmentioning

confidence: 97%

Adaptation of multiple regression analysis to identify effective factors of water losses in water distribution systems

2019

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It is important to manage leaks in water distribution systems by smart water technologies. In order to reduce the water loss, researches on the main factors of water pipe network affecting non-revenue water (NRW) are being actively carried out. In recent years, research has been conducted to estimate NRW using statistical analysis techniques such as Artificial Neural Network (ANN) and Principle Component Analysis (PCA). Research on identifying factors that affect NRW in the target area is actively underway. In this study, Principle components selected through Multiple Regression Analysis are reclassified and applied to NRW estimation using PCA-ANN. The results show that the principal components estimated through PCA are connected to the NRW estimation using ANN. The detailed NRW estimation methodology presented through the study, as a result of simulating PCA-ANN after selecting statistically significant factors by MRA, forward method showed higher NRW estimation accuracy than other MRA methods.

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“…Tabesh et al (2018) have considered the fuzzy logic risk evaluation and Bayesian network methods on three principal components of WDS including apparent losses, real losses and non-revenue water authorized consumptions. The NRW amount prediction has been made through alternative Artificial Neural Networks (ANNs) models by using physical and operational variables such as water supply quantity per demand junction, pipe ratio deterioration, demand energy ratio, average pipe diameter, pipe length per demand junction and number of leaks (Jang & Choi 2017. The network performances have been analyzed by a new risk approach in Kocaeli (Kızılöz & Sisman 2021).…”

Section: Introductionmentioning

confidence: 99%

Non-revenue water ratio prediction with serial triple diagram model

Kizilöz

Şişman

2021

Water Supply

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Water administrations attempt to control the Non-Revenue Water Ratio (NRWR) values in sustainable and well-performing water distribution infrastructures. In this respect, the NRWR value prediction through appropriate models over a small number of controllable variables is significant. The collection, monitoring, and predictions of data on variables that are used in the NRWR calculations are not practical and required significant time besides financial resources. In this study, the NRWR predictions have been made through the suggested method over three parameters. The model prediction accuracies, in the literature obtained by using the Triple Diagram Model (TDM) over two parameters, have been increased through the Serial Triple Diagram Model (STDM) suggested in this study. This method shows that better predictions are possible in the NRWR modeling. Thanks to the model applications developed in this study, water administrations can make predictions with the least error (less than 10% relative error) and certain variables, according to the characteristics of each water distribution network.

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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

Cited by 20 publications

References 14 publications

Prediction model for the leakage rate in a water distribution system

Prediction model for the leakage rate in a water distribution system

Adaptation of multiple regression analysis to identify effective factors of water losses in water distribution systems

Non-revenue water ratio prediction with serial triple diagram model

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