Form of presentation | Articles in international journals and collections |
Year of publication | 2022 |
Язык | русский |
|
Gordeeva Karina Andreevna, author
Okunev Rodion Vladimirovich, author
Smirnova Elena Vasilevna, author
Urazmetov Ildar Anvarovich, author
|
Bibliographic description in the original language |
Giniyatullin K.G., Sakhabiev I.A., Smirnova E.V., Urazmetov I.A., Okunev R.V., Gordeeva K.A. (2022). 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 = Georesources, 24(1), pp. 84–92. DOI: https://doi.org/10.18599/grs.2022.1.8 |
Keywords |
сорбционные свойства почвы, пространственный прогноз, данные дистанционного зондирования Земли, методы машинного обучения |
The name of the journal |
GEORESURSY
|
URL |
https://www.scopus.com/record/display.uri?eid=2-s2.0-85128675408&origin=resultslist&sort=plf-f&src=s&st1=Okunev&st2=R+V&nlo=1&nlr=20&nls=count-f&sid=818902b9840dee051f4a362dfa81a590&sot=anl&sdt=aut&sl=49&s=AU-ID%28%22Okunev%2c+Rodion+Vladimirovich%22+56156815600%29&relpos=0&citeCnt=0&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1 |
Please use this ID to quote from or refer to the card |
https://repository.kpfu.ru/eng/?p_id=266084&p_lang=2 |
Full metadata record |
Field DC |
Value |
Language |
dc.contributor.author |
Gordeeva Karina Andreevna |
ru_RU |
dc.contributor.author |
Okunev Rodion Vladimirovich |
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., Sakhabiev I.A., Smirnova E.V., Urazmetov I.A., Okunev R.V., Gordeeva K.A. (2022). 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 = Georesources, 24(1), pp. 84–92. DOI: https://doi.org/10.18599/grs.2022.1.8 |
ru_RU |
dc.identifier.uri |
https://repository.kpfu.ru/eng/?p_id=266084&p_lang=2 |
ru_RU |
dc.description.abstract |
GEORESURSY |
ru_RU |
dc.language.iso |
ru |
ru_RU |
dc.subject |
сорбционные свойства почвы |
ru_RU |
dc.subject |
пространственный прогноз |
ru_RU |
dc.subject |
данные дистанционного зондирования Земли |
ru_RU |
dc.subject |
методы машинного обучения |
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 |
|