Estimation of soil temperatures with machine learning algorithms-Giresun and Bayburt stations in Turkey

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dc.authoridGULERYUZ, DIDEM/0000-0003-4198-9997
dc.contributor.authorGuleryuz, Didem
dc.date.accessioned2024-10-04T18:49:44Z
dc.date.available2024-10-04T18:49:44Z
dc.date.issued2022
dc.departmentBayburt Üniversitesien_US
dc.description.abstractSince soil temperature (ST) is one of the most critical determinants affecting the soil's physical and chemical properties, the studies on soil temperature estimation increase with the widespread use of deep learning and machine learning algorithms. This study estimates soil temperature at four depths for Giresun and Bayburt stations in Turkey employing the Bayesian Tuned Gaussian Process Regression (BT-GPR), Bayesian Tuned Support Vector Regression (BT-SVR), and Long Short Term Memory (LSTM) models. The stations were selected from semiarid (Bayburt station) and very humid (Giresun station) climates to compare the models' performance and measure their applicability in different climate classes. Common meteorological indicators were determined as input parameters in the developed models, and a five-and-a-half-year daily dataset was used for all models. This paper represents a novel scheme to optimize the hyperparameters of kernel functions for GPR and SVR models using the Bayesian optimization method to expand predictive efficiency. The developed GPR and SVR models' outputs are compared with LSTM via three statistical metrics comprising the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R-2). The results show that the BT-GPR model has a superior estimation ability than other developed models for the two stations. The daily ST estimation with the highest accuracy was obtained at a 5-cm depth using BT-GPR at Giresun station (RMSE = 0.0439, R-2 = 0.9535, MAE = 0.0344 in the testing phase) and Bayburt station (RMSE = 0.0525, R-2 = 0.9438, MAE = 0.0412 in the testing phase). These outcomes provide helpful benchmarking guidance for future soil temperature investigation at various depths across the selected regions.en_US
dc.identifier.doi10.1007/s00704-021-03819-2
dc.identifier.endpage125en_US
dc.identifier.issn0177-798X
dc.identifier.issn1434-4483
dc.identifier.issue1-2en_US
dc.identifier.scopus2-s2.0-85117350063en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage109en_US
dc.identifier.urihttps://doi.org/10.1007/s00704-021-03819-2
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3269
dc.identifier.volume147en_US
dc.identifier.wosWOS:000708806800004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Wienen_US
dc.relation.ispartofTheoretical and Applied Climatologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGaussian Process Regressionen_US
dc.subjectMoistureen_US
dc.subjectClimateen_US
dc.subjectModelsen_US
dc.titleEstimation of soil temperatures with machine learning algorithms-Giresun and Bayburt stations in Turkeyen_US
dc.typeArticleen_US

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