Water distribution system design is inherently associated with hydraulic calculations that require thorough evaluation of obtained results and accuracy of the applied solution. Currently, there are no programs that will replace a designer in these tasks, and there likely will not be such programs. However, some individuals are trying to develop computer programs featuring a certain degree of creativity to facilitate user decision making. In water distribution system design and hydraulic calculations, one should, inter alia, check pressure heads in different parts of the system. It is also important to establish whether the system should contain one or more pressure zones. This determination is connected with the appropriate location of booster and pressure reducing stations. In this paper, the nominal variable is defined. The classes of this variable describe problems related to a value of pressure and division of the water distribution system into pressure zones. By choosing one of the classes, an artificial neural network determines the problems that may arise in a given part of the water distribution system. The classification is conducted based on neural network input variables describing the specific parameters that affect water distribution system design, such as land relief, loss of pressure, pipe roughness and distance to a water supply. The paper presents a new approach that extends traditional methods of hydraulic calculations for water distribution systems by introducing the evaluation of a pressure head and the analysis of design concepts of pressure zones by using artificial neural networks.

The purpose of the study was to present the principles of creating and implementing an integrated water and wastewater management system based on hydrodynamic modeling of water distribution and sewerage disposal systems in small and medium-sized municipal enterprises. The computer modeling of...

This study compares Artificial Neural Networks (ANN) and Elastic Net regression for predicting surface runoff in urban stormwater catchments. Both models were trained on a data set derived from the Stormwater Management Model that included parameters such as imperviousness, flow path width, slope, Manning coefficients, and depression storage. ANN exhibited greater predictive accuracy and stability, especially when modeling nonlinear hydrologic interactions, while Elastic Net offered faster inference and clearer interpretability, but showed reduced accuracy in low-flow conditions. Validation on real-world data revealed the sensitivity of the models to scenarios not fully represented during training. Despite higher computational demands, the ANN proved more adaptable, while the more resource-efficient Elastic Net remains suitable for time-critical or large-scale applications. These findings provide practical insights for urban water resource management, indicating when each approach can be most effectively used in flood risk assessment and stormwater infrastructure planning.

Mathematical models of activated sludge process are well recognised and widely implemented by researchers since 1980’s. There is also numerous software available for modelling and simulation of activated sludge plants, but practical application of those tools is rather limited. One of the main...

Urban stormwater systems face escalating challenges due to climate change, aging infrastructure, and increasing impervious surfaces, necessitating holistic frameworks that integrate hydraulic, structural, and operational factors. This study proposes a fuzzy logic-based controller to evaluate the performance of stormwater drainage systems through three linguistic variables: Hydraulic Performance, Technical Condition, and Operational Condition. The model synthesizes expert knowledge into 125 inference rules, enabling a unified assessment of system reliability. Validated against empirical datasets from European and Chinese drainage networks, the framework demonstrates robust performance across diverse geospatial and operational contexts. Unlike traditional deterministic methods or single-criterion fuzzy systems, the controller addresses interdependencies between hydraulic efficiency, material degradation and external stressors. By translating multi-dimensional uncertainties into actionable maintenance priorities—from “Immediate Replacement” to “No Action Required”—the model enhances decision-making for utilities balancing flood resilience and infrastructure longevity.

Parameterization of SWMM subcatchments is labor-intensive and a major source of model uncertainty. This study presents the Rapid Catchment Generator (RCG), a fuzzy logic framework that derives hydraulic width, average slope, and impervious fraction from three easily accessible descriptors—area, landform type, and land cover type—and inserts them directly into SWMM input files. A sensitivity analysis of 116,640 synthetic simulations confirmed that width, slope, and imperviousness are the dominant controls on runoff and infiltration. Their relationships are encoded in triangular membership functions covering nine geomorphic classes and twelve imperviousness classes, linked through expert-calibrated Mamdani rules. Validation on a calibrated 37-subcatchment, 10-hectare urban basin in Wrocław, Poland, showed Mean Absolute Percentage Errors of 15.9–16.0% for total runoff, 19% for infiltration, and 29–37% for peak flow, while preserving hydrograph shape. RCG thus reduces model setup time and provides a transparent, reproducible starting point for rapid scenario screening and subsequent fine-scale calibration.