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Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems

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Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Letná St. 9, Košice 04001, Slovakia
2
Department of Building Services, Civil Engineering Faculty, Technical University of Kosice, Vysokoskolska St.4, Kosice 04001, Slovakia
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Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2014, 11(8), 8597-8611; https://doi.org/10.3390/ijerph110808597
Received: 3 March 2014 / Revised: 25 July 2014 / Accepted: 29 July 2014 / Published: 21 August 2014
Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. View Full-Text
Keywords: artificial intelligence; hot water system; intelligent buildings; neural network designed on approximate reasoning architecture; neural networks; neural subnetwork; Legionella pneumophila artificial intelligence; hot water system; intelligent buildings; neural network designed on approximate reasoning architecture; neural networks; neural subnetwork; Legionella pneumophila
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MDPI and ACS Style

Sinčak, P.; Ondo, J.; Kaposztasova, D.; Virčikova, M.; Vranayova, Z.; Sabol, J. Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems. Int. J. Environ. Res. Public Health 2014, 11, 8597-8611. https://doi.org/10.3390/ijerph110808597

AMA Style

Sinčak P, Ondo J, Kaposztasova D, Virčikova M, Vranayova Z, Sabol J. Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems. International Journal of Environmental Research and Public Health. 2014; 11(8):8597-8611. https://doi.org/10.3390/ijerph110808597

Chicago/Turabian Style

Sinčak, Peter, Jaroslav Ondo, Daniela Kaposztasova, Maria Virčikova, Zuzana Vranayova, and Jakub Sabol. 2014. "Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems" International Journal of Environmental Research and Public Health 11, no. 8: 8597-8611. https://doi.org/10.3390/ijerph110808597

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