Neural Network Self-Learning Model for Complex Assessment of Drinking Water Safety for Consumers
Tunakova Y., Novikova S., Krasnyuk I. I., Faizullin R., Gabdrakhmanova G.
BioNanoScience
Vol.8, Issue2, P. 504-510
Опубликовано: 2018
Тип ресурса: Статья
DOI:10.1007/s12668-017-0486-z
Аннотация:
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. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.
Ключевые слова:
Integrated risk assessment; Neural networks of Kohonen; Safety of drinking water
Complex networks; Conformal mapping; Health risks; Positive ions; Risk assessment; Safety engineering; Self organizing maps; Integrated assessment; Integrated methodology; Integrated risks; Kohonen; Kohonen self-organizing maps; Neural network method; Safety of drinking water; Self-learning models; Potable water; drinking water; algorithm; Article; artificial neural network; atomic absorption spectrometry; consumer; human; ion chromatography; learning; mathematical analysis; public health; risk assessment
Язык текста: Английский
ISSN: 2191-1649
Tunakova Y.
Novikova S.
Krasnyuk I. I. Ivan Ivanovich 1979-
Faizullin R.
Gabdrakhmanova G.
Тюнакова Y.
Новикова С.
Краснюк И. И. Иван Иванович 1979-
Фаизуллин Р.
Габдрахманова Г.
Neural Network Self-Learning Model for Complex Assessment of Drinking Water Safety for Consumers
Текст визуальный непосредственный
BioNanoScience
Vol.8, Issue2 P. 504-510
2018
Статья
Integrated risk assessment Neural networks of Kohonen Safety of drinking water
Complex networks Conformal mapping Health risks Positive ions Risk assessment Safety engineering Self organizing maps Integrated assessment Integrated methodology Integrated risks Kohonen Kohonen self-organizing maps Neural network method Safety of drinking water Self-learning models Potable water drinking water algorithm Article artificial neural network atomic absorption spectrometry consumer human ion chromatography learning mathematical analysis public health risk assessment
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. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.