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The non-revenue water (NRW) ratio parameter is significantly important for performance evaluation of water distribution systems. In order to evaluate the NRW ratio, the variables influencing this parameter should be determined. Therefore, the first aim of the paper is to define the variables which are influential on the estimation of the NRW ratio and then analyze these variables by using artificial neural networks (ANNs) methodology by means of 50 models with one, two, three, and four-variable input. Secondly, in this study, the NRW ratios have been predicted for the first time by using the Kriging methodology through only two variables. By using the data measured in 12 district meter areas (DMA) in Kocaeli, 60 models in total have been established for NRW ratio prediction through the ANN and Kriging methodologies. The ANN models are closed-box models and therefore the interpretation of the ANN model results requires higher expert opinion. As a consequence, the results show that Kriging model graphs produce much more useful information than ANN models in terms of application and interpretation.

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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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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In this study, the trends and stabilities of temperature and precipitation hydro-meteorology time series recorded since 1870 in Oxford city of England were analyzed in detail. The Innovative Triangular Trend Analysis (ITTA) method has been inspired to identify and analyze the trends and stabilities of the selected time series. To compare the results obtained by the abovementioned method, the Classical Mann Kendall (MK) method has been applied to each series determined for ITTA design. Thanks to the innovative design of ITTA which is preferred by the Classic MK and Sen slope methods, the trends of time series could be analyzed in detail. In this study, the first draft structure has been improved with the help of ±5-±10 % percentage change levels which were added to the ITTA method, and thus more objective evaluations about the trend magnitudes in time series is possible. For the same draft, the monotonic trend slopes which were found by the classical MK were also calculated through the Sen slope method. The data trends could explain in more detail with the help of the draft used in this study, compared to the studies in the literature. Climate change, which has been the most important factor in trend formation in recent years, has been taken into consideration while determining the design series. The thirty-year period up to 2019, a year in which the climate change was felt much more, constitutes the most important reference years for the analysis beginning from 1990, a year in which the climate change effects started to emerge. When the data trends of one hundred fifty years are examined for the different sub-time series, it is seen that the temperature increase in during1990-2019 period is much higher than the past hundred and twenty years, according to the analysis results. The highest average precipitation occurred in the 1990-2019 and 1900-1929 periods, and their amounts and patterns are nearly similar.