Modern solutions in water distribution systems are based on monitoring the quality and quantity of drinking water. Identifying the volume of water consumption is the main element of the tools embedded in water demand forecasting (WDF) systems. The crucial element in forecasting is the influence of random factors on the identification of water consumption, which includes, among others, weather conditions and anthropogenic aspects. The paper proposes an approach to forecasting water demand based on a linear regression model combined with evolutionary strategies to extract weekly seasonality and presents its results. A comparison is made between the author's model and solutions such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Random Forest (RF). The implemented daily forecasting procedure allowed to minimize the MAPE error to even less than 2% for water consumption at the water supply zone level, that is the District Metered Area (DMA). The conducted research may be implemented as a component of WDF systems in water companies, especially at the stage of data preprocessing with the main goal of improving short-term water demand forecasting.

Uncontrolled wastewater discharges have social and environmental consequences and generate increased operational costs. Wastewater treatment plants (WWTPs) are vulnerable infrastructure and require the implementation of risk and safety analyses in the context of climate change and flooding. Therefore, making decisions in changing weather conditions is one of the most important but also most difficult tasks for operators to maintain proper management of wastewater infrastructure facilities. The aim of the research was to develop a decision-making tool based on soft sensor methods to allow inflows to be classified into two classes of WTTP operating conditions. The quality of the regression models was maintained at 90.0%, while the classifier based on the coarse decision tree had a testing accuracy of 92.4%. The results can contribute to a reduction in wastewater load, which is significant in the context of ongoing climate change, and to an improvement in WWTP operation through automation.

The stochastic character of water consumption by consumers and the technical condition of water supply systems are the main deterministic random factors influencing the observed changes in flow rate and pressure. The implementation of Supervisory Control and Data Acquisition (SCADA) systems resulted in the creation of dispersed data sets coming from the devices controlling the operation of the water supply system. Thanks to the use of metadata and advanced computer systems of analysis, data from various sources can be analyzed to detect the operating conditions of the water supply system. The aim of the research was to analyze an empirical exponent, determined on the basis of flow rate and pressure measurements for one of the District Metered Areas (DMAs). Modern supervised and unsupervised machine learning systems were implemented to classify the obtained results. The results of the research showed that on the basis of the established empirical exponent in the systems in which the pressure is reduced at night, it is possible to qualify the operating conditions of the water supply system in the DMA with accuracy of up to 90%. The conducted tests may be implemented as a component of expert diagnostic systems in water companies.

The need for water supply companies to exercise control over the operating conditions of water supply networks has contributed to the development of a number of methods for their diagnosis. The diagnostics of technical infrastructure is a constantly developing field, and therefore, over the years, we have observed the development of various methods of diagnosing network operating conditions and their classification. The article presents, in a synthetic way, the division and review of the previously used methods of diagnosing the operating conditions of water supply networks. The authors also classified and assessed the usefulness of the methods of diagnosis in specific operating conditions. The review carried out by the authors shows that there is a need for research on the detection of operating conditions of water supply networks under the operating conditions of real systems. The results of our deep analysis allow for the understanding of the most important areas of research, as well as the existing worldwide trends in the development of methods for leak diagnosis and detection in water distribution networks. This review is a compendium of knowledge on the detection and diagnosis of water supply networks.

Devices for water consumption measurement provide data from periodical readings in a non-simultaneous and cumulative manner. This may result in inaccuracies within the process of inference about the short-term habitual patterns of water supply network users. Maintaining systems at the interface between periodic and continuous processes requires the continuous improvement of research methodology. To obtain reliable results regarding the variability of water consumption, the first step should be to estimate it for each observation day by periodic averaging and a possible water balancing approach, but the analysis of the value of estimators obtained in this way usually does not allow for studying autocorrelation. However, other methods indicate the existence of multiplicative parameters characterizing short- and long-term variations in water demand. The purpose of this study is to create a new and deterministic method for tackling the problem associated with a lack of short-term detailed data with fuzzy time series using a multiplicative model for water consumption. Satisfactory results have been obtained, demonstrating that the dispersed data, received in a cumulative manner for random periods of measurement, can be analyzed by the methodology of proposed statistical inference. The observed variability in water consumption may be used in the planning and modernization of water supply systems, development of water demand patterns, hydraulic models, and in the creation of forecasting models of water consumption.