This work explores the various relationships between several parameters in an incident hub of a small real WDN. To facilitate analysis, incident data were previously categorised depending on the nature of the cause of the incident. The data are analysed through spatial analysis, based on the transmission of information from the surrounding areas, and are also incorporated to add certain uncertainty to the information. The results of this characterisation are presented and analysed in this contribution. The results are promising in providing water distribution networks with key parameters that promote prediction and classification processes from the lessons learned.

This paper promotes water distribution networks’ (WDNs) sustainability and efficiency by integrating intelligent data analysis with ground-penetrating radar (GPR) to better interpret GPR images for detecting water leaks, favouring their asset assessment. This work uses GPR data from a laboratory setting to investigates the effects of various parameters on image interpretability across pipes. This methodology aims to advance the automation of leak and pipe identification, improving data interpretation and reducing dependency on human experts for leakage detection purposes. The findings suggest the possibility of uncovering new features enhancing GPR image interpretability, presented in 3D models.