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Jafarian, Ahmad, Ghanbary, Fatemeh, saneeifard, Rahim. (1400). Modeling the quality of water and wastewater treatment using neural networks and hybrid neural networks. , 7(34), 51-62.
Ahmad Jafarian; Fatemeh Ghanbary; Rahim saneeifard. "Modeling the quality of water and wastewater treatment using neural networks and hybrid neural networks". , 7, 34, 1400, 51-62.
Jafarian, Ahmad, Ghanbary, Fatemeh, saneeifard, Rahim. (1400). 'Modeling the quality of water and wastewater treatment using neural networks and hybrid neural networks', , 7(34), pp. 51-62.
Jafarian, Ahmad, Ghanbary, Fatemeh, saneeifard, Rahim. Modeling the quality of water and wastewater treatment using neural networks and hybrid neural networks. , 1400; 7(34): 51-62.

Modeling the quality of water and wastewater treatment using neural networks and hybrid neural networks

Journal of New Researches in Mathematics
دوره 7، شماره 34، خرداد و تیر 2022، صفحه 51-62  XML اصل مقاله (547.71 K)
نوع مقاله: research paper
نویسندگان
Ahmad Jafarian email 1؛ Fatemeh Ghanbary2؛ Rahim saneeifardorcid 1
1Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
2Department of Chemitry, Mahabad Branch, Mahabad, Iran
چکیده
One of the most important and fundamental factors in the life of living things is water. Therefore, water pollution is a major environmental problem and prevent water pollution and providing smart methods for water treatment is so important.

Equipping engineering sciences with intelligent tools and artificial intelligence in the diagnose quality of wastewater treatments can reduce the errors of the methods. This paper presents a simple and hybrid neural network with statistical logistic regression method for modelling of the output quality of wastewater treatment. The proposed intelligent method plays an important role in the quality of wastewater treatment and can be used by artificial intelligence researchers and environmental engineers. Comparison of the predicted results by simple neural network and hybrid one showed that the efficiency of the hybrid model and it is suitable for our purpose. results of research proved that the new method has the highest efficiency with minimum errors.
کلیدواژه‌ها
Water pollution؛ mathematical modeling؛ Neural network؛ hybrid neural network؛ statistical logistic regression
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