A multisource dataset for anomaly detection and fault prediction in urban water distribution networks

This multi-source dataset was compiled to support research on anomaly-based leak detection in urban water distribution networks (WDNs). It contains one year of hourly data collected from a Slovak water utility, combining supervisory control and data acquisition (SCADA) measurements (flow and pressure), energy consumption variables (kilowatts, kW; kilovolt-ampere reactive, kvar), and environmental indicators such as groundwater level, temperature, and humidity. All features were transformed into standardized anomaly scores on a 0-100 scale using Elastic Machine Learning and Isolation Forest methods. Confirmed leak records from the utility's operational information system were mapped to binary labels using a ± 7-day temporal window. Feature selection resulted in 18 variables retained based on their statistical association with leak labels using the Goodman-Kruskal gamma coefficient. The dataset can be used for benchmarking anomaly detection and prediction models, evaluating lead-time sensitivity, and developing data-driven early-warning systems. It is also suitable for studies on spatially segmented WDN analysis and is publicly available to support reproducible research.