KFU. Card publication. Emotional artificial neural network (EANN)-based prediction model of maximum A-weighted noise pressure level

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FEDERAL UNIVERSITY

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EMOTIONAL ARTIFICIAL NEURAL NETWORK (EANN)-BASED PREDICTION MODEL OF MAXIMUM A-WEIGHTED NOISE PRESSURE LEVEL

Form of presentation

Articles in international journals and collections

Year of publication

2022

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Authors, employees of KFU

Kuznecov Sergey Viktorovich, author

Sabirova Fayruza Musovna, author

Other authors

Husein Ismail , author

NOMANI M.Z.M. , author

Rahman Ferry Fadzlul , author

Thangavelu: Lakshmi , author

Waluyo Adi Siswanto, author

Zakieva (Suleymanova) Rafina Rafkatovna, author

Melnikova Lyubov Anatolevna, author

Pustokhina Inna Gennadevna, author

Bibliographic description in the original language

Emotional artificial neural network (EANN)-based prediction model of maximum A-weighted noise pressure level / S. V. Kuznetsov, W. A. Siswanto, F. M. Sabirova [et al.] // Noise Mapping. – 2022. – Vol. 9, No. 1. – P. 1-9. – DOI 10.1515/noise-2022-0001. – EDN BBTMGY.

Annotation

Noise is considered one of the most critical environmental issues because it endangers the health of living organisms. For this reason, up-to-date knowledge seeks to find the causes of noise in various industries and thus prevent it as much as possible. Considering the development of railway lines in underdeveloped countries, identifying and modeling the causes of vibrations and noise of rail transportation is of particular importance. The evaluation of railway performance cannot be imagined without measuring and managing noise. This study tried to model the maximum A-weighted noise pressure level with the information obtained from field measurements by Emotional artificial neural network (EANN) models and compare the results with linear and logarithmic regression models. The results showed the high efficiency of EANN models in noise prediction so that the prediction accuracy of 95.6% was reported. The results also showed that in noise prediction based on the neural network-based model, the independent variables of train speed and distance from the center of the route are essential in predicting.

Keywords

Emotional artificial neural network, noise pre-diction, railway, rail Transportation

The name of the journal

NOISE MAPPING

URL

https://www.degruyter.com/document/doi/10.1515/noise-2022-0001/html

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=261807&p_lang=2

Resource files

File name	Size (MB)	Format
F_Staya_Skopus_Nojs_10.1515_noise_2022_0001.pdf	0,68	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Kuznecov Sergey Viktorovich	ru_RU
dc.contributor.author	Sabirova Fayruza Musovna	ru_RU
dc.contributor.author	Husein Ismail	ru_RU
dc.contributor.author	NOMANI M.Z.M.	ru_RU
dc.contributor.author	Rahman Ferry Fadzlul	ru_RU
dc.contributor.author	Thangavelu: Lakshmi	ru_RU
dc.contributor.author	Waluyo Adi Siswanto	ru_RU
dc.contributor.author	Zakieva (Suleymanova) Rafina Rafkatovna	ru_RU
dc.contributor.author	Melnikova Lyubov Anatolevna	ru_RU
dc.contributor.author	Pustokhina Inna Gennadevna	ru_RU
dc.date.accessioned	2022-01-01T00:00:00Z	ru_RU
dc.date.available	2022-01-01T00:00:00Z	ru_RU
dc.date.issued	2022	ru_RU
dc.identifier.citation	Emotional artificial neural network (EANN)-based prediction model of maximum A-weighted noise pressure level / S. V. Kuznetsov, W. A. Siswanto, F. M. Sabirova [et al.] // Noise Mapping. – 2022. – Vol. 9, No. 1. – P. 1-9. – DOI 10.1515/noise-2022-0001. – EDN BBTMGY.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=261807&p_lang=2	ru_RU
dc.description.abstract	NOISE MAPPING	ru_RU
dc.description.abstract	Noise is considered one of the most critical environmental issues because it endangers the health of living organisms. For this reason, up-to-date knowledge seeks to find the causes of noise in various industries and thus prevent it as much as possible. Considering the development of railway lines in underdeveloped countries, identifying and modeling the causes of vibrations and noise of rail transportation is of particular importance. The evaluation of railway performance cannot be imagined without measuring and managing noise. This study tried to model the maximum A-weighted noise pressure level with the information obtained from field measurements by Emotional artificial neural network (EANN) models and compare the results with linear and logarithmic regression models. The results showed the high efficiency of EANN models in noise prediction so that the prediction accuracy of 95.6% was reported. The results also showed that in noise prediction based on the neural network-based model, the independent variables of train speed and distance from the center of the route are essential in predicting.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Emotional artificial neural network	ru_RU
dc.subject	noise pre-diction	ru_RU
dc.subject	railway	ru_RU
dc.subject	rail Transportation	ru_RU
dc.title	Emotional artificial neural network (EANN)-based prediction model of maximum A-weighted noise pressure level	ru_RU
dc.type	Articles in international journals and collections	ru_RU