Smart City Gnosys

Smart city article details

Title	A Preliminary Solution For Anomaly Detection In Water Quality Monitoring
ID_Doc	3760
Authors	Bourelly C.; Bria A.; Ferrigno L.; Gerevini L.; Marrocco C.; Molinara M.; Cerro G.; Cicalini M.; Ria A.
Year	2020
Published	Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020
DOI	http://dx.doi.org/10.1109/SMARTCOMP50058.2020.00086
Abstract	In smart city framework, the water monitoring through an efficient, low-cost, low-power and IoT-oriented sensor technology is a crucial aspect to allow, with limited resources, the analysis of contaminants eventually affecting wastewater. In this sense, common interfering substances, as detergents, cannot be classified as dangerous contaminants and should be neglected in the classification. By adopting classical machine learning approaches having a finite set of possible responses, each alteration of the sensor baseline is always classified as one out of the predetermined substances. Consequently, we developed an anomaly detection system based on one-class classifiers, able to discriminate between a recognized set of substances and an interfering source. In this way, the proposed detection system is able to provide detailed information about the water status and distinguish between harmless detergents and dangerous contaminants. © 2020 IEEE.
Author Keywords	artificial neural network; machine learning; sensor network; water monitoring

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