Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Kamran Azizi"'
Publikováno v:
International Journal of Digital Earth, Vol 16, Iss 1, Pp 1752-1769 (2023)
In this study some soil phosphorous sorption parameters (PSPs) by using different machine learning models (Cubist (Cu), random forest (RF), support vector machines (SVM) and Gaussian process regression (GPR)) were predicted. The results showed that u
Externí odkaz:
https://doaj.org/article/86a74a944db249a78b369c4cc046b63e
Publikováno v:
فناوریهای پیشرفته در بهرهوری آب, Vol 2, Iss 4, Pp 68-87 (2023)
In areas with dry and semi-arid climates, underground water is one of the main sources of water supply for agriculture, industry and drinking. The aim of this research is to investigate hydrodynamic coefficients, plain balance and aquifer thickness i
Externí odkaz:
https://doaj.org/article/8d8064a16eeb44939e6fe3892f3b3f13
Publikováno v:
Sensors, Vol 22, Iss 18, p 6890 (2022)
This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((ran
Externí odkaz:
https://doaj.org/article/ab0d23cfc77f40ed9ec327702af301db
Publikováno v:
Soil and Tillage Research. 229:105681
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::982101b7994a8ecf52c365a829175cfc
https://doi.org/10.1016/j.still.2023.105681
https://doi.org/10.1016/j.still.2023.105681
Publikováno v:
Journal of Applied Geophysics. 210:104944
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2320eb92f82ddea8a47d00e83cf27894
https://doi.org/10.1016/j.jappgeo.2023.104944
https://doi.org/10.1016/j.jappgeo.2023.104944
Publikováno v:
Azizi, K, Ayoubi, S, Nabiollahi, K, Garosi, Y & Gislum, R 2022, ' Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran ', Journal of Geochemical Exploration, vol. 233, 106921 . https://doi.org/10.1016/j.gexplo.2021.106921
The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe, Cu, Mn) in western Iran, using environmental covariates and applying two machine learning methods comprised Random forest (RF), and Cubist. In this respect,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8564db2931be222cd6fe0d4fca34e111
https://pure.au.dk/portal/da/publications/predicting-heavy-metal-contents-by-applying-machine-learning-approaches-and-environmental-covariates-in-west-of-iran(087e8d06-c699-494a-9bcb-c3a8aedbf83c).html
https://pure.au.dk/portal/da/publications/predicting-heavy-metal-contents-by-applying-machine-learning-approaches-and-environmental-covariates-in-west-of-iran(087e8d06-c699-494a-9bcb-c3a8aedbf83c).html
