Water quality management is a key area to guarantee drinking water safety to users. This task is based on disinfection techniques, such as chlorination, applied to the drinking water network to prevent the growth of microorganisms present in the water. The continuous monitoring of water quality parameters is fundamental to assess the sanitary conditions of the drinking water and to detect unexpected events. The whole process is based on the assumption that the information retrieved from quality sensors is totally reliable, but due to the complexity of the calibration and maintenance of these chemical sensors, several factors affect the accuracy of the raw data collected. Consequently, any decision might be based on a non-solid base. Therefore, this work presents a data analytics monitoring methodology based on temporal and spatial models to discover if a sensor is detecting a real change in water quality parameters or is actually providing inconsistent information due to some malfunction. The methodology presented anticipated by 12.4 days, on average, the detection of a sensor problem before the fault was reported by the water utilities expert using knowledge accumulated with visual analysis. The proposed methodology has been satisfactorily tested on the Barcelona drinking water network.
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