Trend-risk model for predicting non-revenue water: An application in Turkey

Şişman, Eyüp; Kizilöz, Burak

doi:10.1016/j.jup.2020.101137

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…The applications of the VINAM procedure are presented for two well-known models, which are artificial neural network (ANN) and adaptive network based fuzzy inference system (ANFIS). 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).…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…For ANN model performance, 55% of the available data is arranged as training, 35% as validation and 10% as testing. These models are developed with one hidden layer including four neurons and feed forward back propagation training procedure with support of Levenberg-Marquardt back propagation algorithm (Coulibaly et al 2000;Kermani et al 2005;Kızılöz et al 2015;Rahman et al 2019;Sisman & Kizilöz 2020a, 2020b.…”

Section: Resultsmentioning

confidence: 99%

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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%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A new innovative method for model efficiency performance

2021

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“…Although many studies reported high fluctuations of NRW [ 4 , 7 , 10 , 11 ], no logical reasoning has been provided, possibly because most researchers focused on filling the IWA/AWWA water balance table [ 11 ]. Güngör-Demirci and Lee [ 12 ] applied the fixed effects panel regression model to understand the impacts of independent variables on NRW; however, they did not evaluate the influence of water consumption.…”

Section: Introductionmentioning

confidence: 99%