This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters.

This paper presented the architecture and construction of a novel smart building system that could monitor and control buildings’ use in a safe and optimal way. The system operates on a Raspberry local server, which could be connected via the cloud technology to a central platform. The local system includes nine modules that inter-communicate. The system detects sensor faults, and provides a friendly interface to occupants. The paper presented the software architecture IoT used for the building monitoring and the use of this system for the management of fifteen social housing units during a year. The system allowed the investigation of indoor comfort and both energy and hot water consumptions. Data analysis resulted in the detection of abnormal energy consumptions. The system could be easily used in buildings’ management. It works in a plug-and-play mode.

The increase in rainfall intensity caused by climate change, combined with high levels of urban soil sealing and the limited capacity of drainage systems, is significantly increasing flooding risk. Integrated stormwater management is a key factor in dealing with the consequences of climate change by mitigating flood risk based on nature-based solutions. An experimental site was designed to assess the hydrological response of vegetated swales depending on different catchment surfaces, and to evaluate the impact of spatial variability of the hydraulic conductivity (K). A hydrological model was developed on EPA SWMM and calibrated based on measured data in two experimental swales with different active surfaces (N6—50 m² and N11—100 m²). The model validation was assessed with NSE scores higher than 0.7. The simulations considered various factors, such as the water level in the swales, the actual rainfall, the evapotranspiration, the swale geometry, the catchment area (Sa), and the hydraulic conductivities of the natural silty soil, to assess, for the heaviest rainfall event, the best swale morphological characteristics. The study examined the combined impact of K (8) and Sa (6) on swale storage capacity, designed with a 250 mm depth. The simulations showed that the 250 mm overflow limit was exceeded for N10 (90 m²) when K ≤ 2 × 10⁻⁶ m/s, and for N11 (100 m²) when K ≤ 4 × 10⁻⁶ m/s. These results provide valuable information on the optimal storage capacity based on the swales’ geometrical and physical properties.

Losses of non-revenue water (NRW) due to undetected leaks threaten the sustainability and operational efficiency of water distribution systems. This study presents an adaptive real-time DBSCAN-Leak algorithm. This algorithm enhances the conventional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method for anomaly detection in water distribution systems. Unlike traditional approaches that require static thresholds or complex hydraulic models, DBSCAN-Leak relies solely on flow measurements and dynamically adapts to day-of-week and hour-of-day consumption patterns. The framework utilizes a dynamic neighborhood radius and statistical confidence intervals to estimate the leak probability in real-time. The algorithm was validated using the flow data from the Lille University Scientific Campus. Compared to the original DBSCAN, DBSCAN-Leak achieved significant improvements, detecting 16 confirmed leaks versus one, with 100% precision, 98% accuracy, 80% recall, and an F1-score of 89%. These results demonstrate a low-cost, scalable solution that requires no additional hardware. This approach offers encouraging potential for utility-scale implementation and is extensible to other infrastructure networks, such as gas or electricity distribution networks.