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.

Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using support vector regression (SVR) and multilayer perceptron (MLP) for three wells in the Greater Poland Province, where the largest water deficits occur, in the period 1975–2014. The results of this method were compared with those of the regression method, general regression model. For the case study under investigation, the Hellwig method provided the best set of predictors consisted of GWL at lags of −1 and −2 months, precipitation from the current month, and delayed from −1 to −6 months, and past temperature at −1, −3, −4 and −6 months. Such input led to a model accuracy of 0.003–0.022 for a mean squared error and r2 of >0.8. The results obtained with SVR were slightly better than those with MLP. Moreover, every well required an individual set of predictors, and additional meteorological inputs improved the models’ performance.

Heating and cooling degree hours (HDH and CDH) are weather-based technical indexes designed to describe the need for energy requirements of buildings. Their calculation is the simplest method to estimate energy demand, providing the pattern of internal temperature variations in a building in response to weather conditions. The aim of the study is HDH and CDH prediction for Wrocław, Poland, based on outdoor air temperature using machine learning methods: artificial neural networks and support vector regression (ANN and SVR). The key issues raise in the study are: a detailed analysis of the most significant temperature lags (from 1 to 24 past hours) serving as predictors for modelling and an assessment of the impact of the database clustering on its accuracy. The best results are obtained with the clustering approach. The best predictor is the outdoor temperature observed 1 and 24 h before forecast demand (R² = 0.981 and 0.904 for heating degree and cooling degree hours indices, respectively). Models with the highest quality are created using ANN, and the lowest with SVR. Prediction of heating/cooling degree hour indices provides building demand in advance, does not require knowledge about its characteristics, and expresses the possible impact of regional climate modifications.

The aim of rainfall prediction in urban areas is to provide information about precipitation, which may cause flooding resulting from the insufficient capacity of sewage systems. The research combined cluster analysis and support vector regression (CSVR) to create a simpler, and as effective as hybrid models, method predicting daily rainfall in Wroclaw, Poland. Results demonstrate that the clustering approach while modelling is reasonable, improves the quality of prediction and minimises error values. The best SVR performance, RMSE = 2.492 mm and r² = 0.830 in the testing subset, was obtained for the Ward clustering method. Nevertheless, comparisons with MLP prediction, combined with k-means clustering, proved to be slightly more accurate and led to the creation of a model with RMSE = 1.974 and r² = 0.899. The presented approach might constitute an alternative method to be used for creating rainfall patterns in stormwater discharge or management systems leading to an increase in flash flood safety in an urban catchment.

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 use of plants and natural processes for wastewater treatment is an issue that arouses interest among technologists and scientists around the world. The aim of the article was to analyze the influence of the air temperature and insolation on the removal of nitrate nitrogen from the wastewater treated in the hydroponic system, under greenhouse conditions. Samples of sewage for its quality tests were taken from a wastewater treatment plant (WWTP) located in the southwestern part of Poland. Data regarding daily sunshine duration and average daily air temperature values in selected periods of 2013–2016 come from a meteorological station located 30 km from WWTP. The conducted research and analyses of the results clearly indicate that under moderate climate conditions, the amount of solar radiation reaching the Earth’s surface is insufficient to ensure the year-round, effective wastewater treatment process in the hydroponic system. In the case of air temperature, no correlation was found between the tested parameters, which indicates the lack of temperature influence on the efficiency of NO₃ removal from the wastewater by macrophytes growing in the lagoon.