A novel machine learning approach: Soil temperature ordinal classification (STOC)

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dc.authoridBirant, Derya/0000-0003-3138-0432
dc.authorwosidBirant, Derya/U-6211-2017
dc.contributor.authorKucuk, Cansel
dc.contributor.authorBirant, Derya
dc.contributor.authorYıldırım Taşer, Pelin
dc.date.accessioned2023-03-22T19:47:19Z
dc.date.available2023-03-22T19:47:19Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractSoil temperature prediction is an important task since soil temperature plays an important role in agriculture and land use. Although some progress has been made in this area, the existing methods provide a regression or nominal classification task. However, ordinal classification is yet to be explored. To bridge the gap, this paper proposes a novel approach: Soil Temperature Ordinal Classification (STOC), which considers the relationships between the class labels during soil temperature level prediction. To demonstrate the effectiveness of the proposed approach, the STOC method using five different traditional machine learning methods (Decision Tree, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, and Random Forest) was applied on daily values of meteorological and soil data obtained from 16 stations in three states (Utah, Alabama, and New Mexico) of United States at five soil depths (2, 4, 8, 20, and 40 inches) between the years of 2011 and 2020. The experiments show that the proposed STOC approach is an efficient method for soil temperature level (very low, low, medium, high, and very high) prediction. The applied STOC models (STOC.DT, STOC.NB, STOC.KNN, STOC.SVM, and STOC.RF) showed average accuracy rates of 90.95%, 77.09%, 90.84%, 89.94%, and 90.91% on the experimental datasets, respectively. It was observed from the experimental results that the STOC.DT method achieved the best soil temperature level prediction among the others.en_US
dc.identifier.doi10.15832/ankutbd.866045
dc.identifier.endpage649en_US
dc.identifier.issn1300-7580
dc.identifier.issn2148-9297
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85141395333en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage635en_US
dc.identifier.trdizinid1138045en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14034/610
dc.identifier.urihttps://doi.org/10.15832/ankutbd.866045
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1138045
dc.identifier.volume28en_US
dc.identifier.wosWOS:000880378700008en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherAnkara Univ, Fac Agren_US
dc.relation.journalJournal Of Agricultural Sciences-Tarim Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAgricultureen_US
dc.subjectClassificationen_US
dc.subjectDecision treeen_US
dc.subjectMachine learningen_US
dc.subjectRandom foresten_US
dc.subjectSoil temperature levelen_US
dc.subjectTime-Seriesen_US
dc.subjectPredictionen_US
dc.subjectModelen_US
dc.subjectRegressionen_US
dc.titleA novel machine learning approach: Soil temperature ordinal classification (STOC)en_US
dc.typeArticleen_US

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