KFU. Card publication. Digital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learning

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DIGITAL MAPPING OF INDICATORS THAT DETERMINE THE SORPTION PROPERTIES OF SOILS IN RELATION TO POLLUTANTS, ACCORDING TO REMOTE SENSING DATA OF THE EARTH USING MACHINE LEARNING

Form of presentation

Articles in international journals and collections

Year of publication

2022

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Authors, employees of KFU

Giniyatullin Kamil Gashikovich, author

Gordeeva Karina Andreevna, author

Okunev Rodion Vladimirovich, author

Sakhabiev Ilnaz Alimovich, author

Smirnova Elena Vasilevna, author

Urazmetov Ildar Anvarovich, author

Bibliographic description in the original language

Giniyatullin, K.G., Sahabiev, I.A., Smirnova, E.V., Urazmetov, I.A., Okunev, R.V., Gordeeva, K.A., Digital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learning//Georesursy. - 2022. - Vol.24, Is.1. - P.84-92.

Keywords

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The name of the journal

GEORESURSY

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128675408&doi=10.18599%2fgrs.2022.1.8&partnerID=40&md5=f2854ea5772c7a91d047fff41ac51297

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

https://repository.kpfu.ru/eng/?p_id=266601&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Giniyatullin Kamil Gashikovich	ru_RU
dc.contributor.author	Gordeeva Karina Andreevna	ru_RU
dc.contributor.author	Okunev Rodion Vladimirovich	ru_RU
dc.contributor.author	Sakhabiev Ilnaz Alimovich	ru_RU
dc.contributor.author	Smirnova Elena Vasilevna	ru_RU
dc.contributor.author	Urazmetov Ildar Anvarovich	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	Giniyatullin, K.G., Sahabiev, I.A., Smirnova, E.V., Urazmetov, I.A., Okunev, R.V., Gordeeva, K.A., Digital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learning//Georesursy. - 2022. - Vol.24, Is.1. - P.84-92.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=266601&p_lang=2	ru_RU
dc.description.abstract	GEORESURSY	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	��	ru_RU
dc.subject	��	ru_RU
dc.title	Digital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learning	ru_RU
dc.type	Articles in international journals and collections	ru_RU