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Advanced machine learning model for better prediction accuracy of soil temperature at different depths

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dc.citedby45
dc.contributor.authorAlizamir M.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMert C.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorKim S.en_US
dc.contributor.authorKim N.W.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57188682009en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid54898539300en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid36349835400en_US
dc.contributor.authorid34770123000en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:10:29Z
dc.date.available2023-05-29T08:10:29Z
dc.date.issued2020
dc.descriptionair temperature; article; artificial neural network; atmosphere; chemical reaction; controlled study; ecosystem; linear regression analysis; prediction; soil depth; soil temperature; solar radiation; wind speed; chemistry; ecosystem; environmental monitoring; machine learning; river; soil; statistical model; temperature; Atmosphere; Ecosystem; Environmental Monitoring; Linear Models; Machine Learning; Neural Networks, Computer; Rivers; Soil; Temperatureen_US
dc.description.abstractSoil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models' outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm. � 2020 Alizamir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe0231055
dc.identifier.doi10.1371/journal.pone.0231055
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85083389932
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85083389932&doi=10.1371%2fjournal.pone.0231055&partnerID=40&md5=af5e7e065e77ef426d65b63e53eb138b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25525
dc.identifier.volume15
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitlePLoS ONE
dc.titleAdvanced machine learning model for better prediction accuracy of soil temperature at different depthsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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