The success of the analysis and design of a Water Network (WN) is strongly dependent on the veracity of the data and a priori knowledge used in the model calibration of the network. This fact motivates this paper in which an off-line approach to verify datasets acquired from WN is proposed. This approach allows the data separation of abnormal and normal events without requiring high expertise for a large raw database. The core of the approach is an unsupervised classification tool that does not require the features of the different events to be identified. The proposal is applied to datasets acquired from a Mexican water management utility located in the center part of Mexico. The datasets are pre-processed to be synchronized since they were recorded and sent with different and irregular sampling times to a web platform. The pressures and flow-rate conforming the datasets correspond to the dates between 25 June 2019 @ 00:00 and 25 September 2019 @ 00:00. The District Metered Area (DMA) is formed by 90 nodes and 78 pipes, and it provides service to approximately 2000 consumers. The raw data identified as generated by abnormal events are validated with the reports of the DMA managers. The abnormal events identified are communication problems, sensor failures, and draining of the network reservoir.
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