Integrating ATR-FTIR and data-driven models to predict total soil carbon and nitrogen towards sustainable watershed management

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dc.authorid0000-0003-1099-4363en_US
dc.authorid0000-0002-0156-1657en_US
dc.contributor.authorAslan-Sungur, Güler
dc.contributor.authorEvrendilek, Fatih
dc.contributor.authorKarakaya, Nusret
dc.contributor.authorGüngör, Kerem
dc.contributor.authorKılıç, Sinan
dc.date.accessioned2021-06-23T19:34:32Z
dc.date.available2021-06-23T19:34:32Z
dc.date.issued2013
dc.departmentBAİBÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractThe use of Attenuated Total Reflectance (ATR) is an alternative method in determining carbon (C), nitrogen (N) and other elemental contents of organic and inorganic soils for which diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy has been mostly utilized. In this study, the combined use of ATR-Fourier transform infrared (FTIR) spectroscopy and partial least square regression (PLSR) or artificial neural network (ANN) models in estimating total soil C and N have been explored which provide direct, rapid, economical and multiple in situ measurements. Total soil C and N data obtained from 153 soil samples across agricultural lands and analyzed using CNH elemental analyzer were used to build PLSR and ANN models as a function of ATR-FTIR spectrum ranges based on a training dataset with leave-one-out cross validation (LCV) and independent validation (IV) dataset that randomly constitute 67% and 33% of the entire dataset respectively. Wavenumber ranges of 650-2365 cm(-1) and 773-1726 cm(-1) in ATR-FTIR data were selected as predictors for PLSR and ANN models of soil C respectively. PLSR model of soil C led to r(2) = 0.86 for training and r(2) = 0.68 for validation, with PLSR model of soil N as a result of wavenumber range of 1300-1400 cm(-1) leading to r(2) = 0.81 for training and r(2) < 0.1 for validation. Multilayer perceptron model appeared to be the best-performing ANN for the emulations of both total soil C and N and outperformed PLSR model of total soil N.en_US
dc.identifier.endpage11en_US
dc.identifier.issn0972-0626
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-84880663319en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage5en_US
dc.identifier.urihttps://worldresearchersassociations.com/Archives/RJCE/Vol(17)2013/June2013.aspx
dc.identifier.urihttps://hdl.handle.net/20.500.12491/7545
dc.identifier.volume17en_US
dc.identifier.wosWOS:000319408900002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAslan-Sungur, Güler
dc.institutionauthorEvrendilek, Fatih
dc.institutionauthorKarakaya, Nusret
dc.institutionauthorGüngör, Kerem
dc.language.isoenen_US
dc.publisherDr Jyoti Gargen_US
dc.relation.ispartofResearch Journal Of Chemistry And Environmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural networken_US
dc.subjectEnvironmental Monitoringen_US
dc.subjectPartial Least Square Regressionen_US
dc.subjectSoil Managementen_US
dc.titleIntegrating ATR-FTIR and data-driven models to predict total soil carbon and nitrogen towards sustainable watershed managementen_US
dc.typeArticleen_US

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