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Neural Network Self-Learning Model for Complex Assessment of Drinking Water Safety for Consumers

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Abstract

We need to take into complex assessment a set of influencing factors of drinking water safety. This raises the task of developing an integrated methodology assessing the safety of drinking water that reaches the consumers. For the integrated assessment of the safety of drinking water, the method of clustering was chosen, namely, the neural network method of Kohonen self-organizing maps. Zones were separated by the method of cluster neural network analysis. The zones are characterized by different content of metal cations in drinking water, levels of carcinogenic and non-carcinogenic risk to the health of the child population, and the probability of the receipt of metal cations with potable water to consumers.

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Funding

This study was supported by the Program of Competitive Growth of Kazan Federal University and subsidy was allocated to Kazan Federal University for the state assignment in the sphere of scientific activities.

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Authors and Affiliations

  1. Kazan National Research Technical University named after A. N. Tupolev, Kazan, Russia

    Yulia Tunakova, Svetlana Novikova & Gulnara Gabdrakhmanova

  2. I.M. Sechenov First Moscow State Medical University, Moscow, Russia

    Ivan Krasnyuk

  3. Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia

    Rashat Faizullin

Authors
  1. Yulia Tunakova
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  2. Svetlana Novikova
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  3. Ivan Krasnyuk
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  4. Rashat Faizullin
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  5. Gulnara Gabdrakhmanova
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Corresponding author

Correspondence to Yulia Tunakova.

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Tunakova, Y., Novikova, S., Krasnyuk, I. et al. Neural Network Self-Learning Model for Complex Assessment of Drinking Water Safety for Consumers. BioNanoSci. 8, 504–510 (2018). https://doi.org/10.1007/s12668-017-0486-z

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