AQUALEARN aims to develop and demonstrate the application of digital twins based on machine learning to detect real-time anomalies in supply systems (breaks, obstructions, air pockets). The methodology includes detection/classification of malfunctions using continuous monitoring and location/quantification using pressure records. Implementation recommendations will be established.