Exceedance probabilities of non-revenue water and performance analysis

Kizilöz, Burak; Şişman, Eyüp

doi:10.1007/s13762-020-03018-y

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…These applications are based on the water losses measurement in potable water distribution systems, for which water loss predictions are among the most important issues of water stress control (Sisman & Kizilöz 2020a, 2020b. The most important component in the evaluation of a water distribution system with regards to water losses is the non-revenue water (Kanakoudis & Muhammetoglu 2014;Boztaşet al 2019;Sisman & Kizilöz 2020a, 2020bKizilöz & Sisman 2021). Jang & Choi (2017) built a model to calculate the NRW ratio of Incheon, Republic of Korea by means of ANN methodology.…”

Section: Resultsmentioning

confidence: 99%

A new innovative method for model efficiency performance

2021

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In every aspect of scientific research, model predictions need calibration and validation as their representativity of the record measurement. In the literature, there are a myriad of formulations, empirical expressions, algorithms and software for model efficiency assessment. In general, model predictions are curve fitting procedures with a set of assumptions that are not cared for sensitively in many studies, but only a single value comparison between the measurements and predictions is taken into consideration, and then the researcher makes the decision as for the model efficiency. Among the classical statistical efficiency formulations, the most widely used ones are bias (BI), mean square error (MSE), correlation coefficient (CC) and Nash-Sutcliffe efficiency (NSE) procedures all of which are embedded within the visual inspection and numerical analysis (VINAM) square graph as measurements versus predictions scatter diagram. The VINAM provides a set of verbal interpretations and then numerical improvements embracing all the previous statistical efficiency formulations. The fundamental criterion in the VINAM is 1:1 (45°) main diagonal along which all visual, science philosophical, logical, rational and mathematical procedures boil down for model validation. The application of the VINAM approach is presented for artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) model predictions.

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Section: Resultsmentioning

confidence: 99%

A new innovative method for model efficiency performance

2021

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“…Here, 67 sections of DMAs and 84 sections of PMAs, as shown in Figure 3, were installed in 2014 to reduce the water loss rate of 45.40%. While the total network length of the district is 1,114 km (Kizilöz & Sisman 2021), the network length in the DMAs is 56,639 km. In addition, all water meters in the DMAs have been replaced entirely by smart water metres to remove the apparent loss effect.…”

Section: Data Collection and Description Of The Study Areamentioning

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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Mathematical Modeling Principles

Şen

2023

Shallow and Deep Learning Principles

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Exceedance probabilities of non-revenue water and performance analysis

Cited by 5 publications

References 27 publications

A new innovative method for model efficiency performance

A new innovative method for model efficiency performance

Prediction model for the leakage rate in a water distribution system

Mathematical Modeling Principles

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