Water utilities increasingly require a deeper understanding of users' water demand to optimize resource management and meet customer needs. The adoption of smart metering solutions enables the collection of detailed consumption data. This study presents a methodology for identifying usage patterns by analysing smart meter data. In particular, the authors introduce a machine learning methodology to identify user profiles from smart meter water consumption data. Clustering results are graphically interpreted and compared, providing insights into consumption habits and assessing the methodology's effectiveness and stability.

The rapid urbanization of our world has led to an ever-increasing urban population, resulting in significant challenges in managing transportation systems. Traffic congestion, with its associated environmental pollution, safety hazards, and prolonged travel times, continues to plague urban areas. Despite numerous efforts to mitigate these issues, the problem persists and hampers urban development. This paper addresses the pressing need for effective traffic flow forecasting of the city of Catania as a critical element in traffic management. Catania’s rapid urbanization has created a complex transportation network as the city’s population has expanded into suburbs and neighbouring areas, causing congestion, pollution, and increased individual motorization due to the high demand for mobility. This has led to severe traffic congestion in the central district, necessitating an efficient traffic management strategy, particularly through the utilization of traffic flow forecasting. A central challenge in traffic flow prediction is the cost and practicality of sensors deployment on every road. To address this issue, the authors propose a novel two-level machine learning approach. The first level employs an unsupervised clustering model to extract patterns from big data generated by sensors, while the second level employs supervised machine learning models for traffic flow forecasting within each cluster. Importantly, this approach enables predictions for roads lacking sensor data by utilizing a really small subset of these new data from alternative sources and assigning roads to appropriate clusters.