The last decade has seen the development of complex IT systems to support city management, i.e., the creation of so-called intelligent cities. These systems include modules dedicated to particular branches of municipal economy, such as urban transport, heating systems, energy systems, telecommunications, and finally water and sewage management. In turn, with regard to the latter branch, IT systems supporting the management of water supply and sewage networks and sewage treatment plants are being developed. This paper deals with the system concerning the urban water supply network, and in particular, with the subsystem for detecting and locating leakages on the water supply network, including so-called hidden leakages. These leaks cause the greatest water losses in networks, especially in old ones, with a very diverse age and material structure. In the proposed concept of the subsystem consisting of a GIS (Geographical Information System), SCADA (Supervisory Control and Data Acquisition) system and hydraulic model of the water supply network, an algorithm of leak detection and location based on the neural networks’ MLP (multi-layer perceptron) and Kohonen was developed. The algorithm has been tested on the hydraulic models of several municipal water supply networks.

This article presents several algorithms for controlling water supply system pumps. The aim of having control is the hydraulic optimisation of the network, i.e., ensuring the desired pressure in its recipient nodes, and minimising energy costs of network operation. These two tasks belong to the key issues related to the management and operation of water supply networks, apart from the reduction in water losses caused by network failures and ensuring proper water quality. The presented algorithms have been implemented in an Information and Communications Technology (ICT) system developed at the Systems Research Institute of the Polish Academy of Sciences (IBS PAN) and implemented in the waterworks GPW S.A. in Katowice/Poland.

This paper presents the concepts of a probabilistic model for storm overflow discharges, in which arbitrary dynamics of the catchment urbanization were included in the assumed period covered by calculations. This model is composed of three components. The first constitutes the classification model for the forecast of storm overflow discharges, in which its operation was related to rainfall characteristics, catchment retention, as well as the degree of imperviousness. The second component is a synthetic precipitation generator, serving for the simulation of long-term observation series. The third component of the model includes the functions of dynamic changes in the methods of the catchment development. It allows for the simulation of changes in the extent of imperviousness of the catchment in the long-term perspective. This is an important advantage of the model, because it gives the possibility of forecasting (dynamic control) of catchment retention, accounting for the quantitative criteria and their potential changes in the long-term perspective in relation to the number of storm overflows. Analyses carried out in the research revealed that the empirical coefficients included in the logit model have a physical interpretation, which makes it possible to apply the obtained model to other catchments. The paper also shows the use of the prepared probabilistic model for rational catchment management, with respect to the forecasted number of storm overflow discharges in the long-term and short-term perspective. The model given in the work can be also applied to the design and monitoring of catchment retention in such a way that in the progressive climatic changes and urbanization of the catchment, the number of storm overflow discharges remains within the established range.

This article analyzes the effect that the physicochemical parameters of the wastewater flowing into a treatment plant have on the activated sludge settleability. The statistical analysis shows that as far as the technological parameters are concerned, the activated sludge sedimentation capacity is mostly affected by the biomass concentration in the chamber, whereas as for the abiotic factors, settleability is significantly determined by the season of the year and thus the temperature. With regard to the wastewater quality-related parameters, biological oxygen demand has the greatest effect on settleability. The conducted analyzes involved the development of statistical models to predict the activated sludge sedimentation capacity on the basis of multiple linear regression and genetic programming.