Estimation of tree height with machine learning techniques in coppice-originated pure sessile oak (Quercus petraea (Matt.) Liebl.) stands

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dc.contributor.authorŞahin, Abbas
dc.contributor.authorÖzdemir, Gafura Aylak
dc.contributor.authorOral, Okan
dc.contributor.authorAylak, Batin Latif
dc.contributor.authorİnce, Murat
dc.contributor.authorÖzdemir, Emrah
dc.date.accessioned2025-02-06T15:17:55Z
dc.date.available2025-02-06T15:17:55Z
dc.date.issued2023
dc.departmentTAÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractIn this study, in order to estimate total tree height, three di?erent model structures with di?erentinput variables were produced through the use of 872 tree data points obtained from di?erentdevelopment stages and sites in coppice-originated pure sessile oak (Quercus petraea [Matt.] Liebl.)stands. These models were ?tted with machine learning techniques such as arti?cial neuralnetworks (ANNs), decision trees, support vector machines, and random forests. In addition, themodel based on DBH was ?tted and its parameters were calculated using the ordinary nonlinearleast squares method and this model was selected as the best model in Model 1. In other modelstructures, ANN model was chosen as the best estimation method based on the relative rankingmethod in which the goodness of ?t statistics of the estimation methods were evaluated together.The inclusion of stand variables in addition to the DBH measurement in the model increased theR2 by about 36% and reduced the error rate by 55%.
dc.identifier.citationŞahin, A., Özdemir, Gafura A., Oral, O., Aylak, Batin L., İnce, M., Özdemir, E. (2023). Estimation of tree height with machine learning techniques in coppice-originated pure sessile oak (Quercus petraea (Matt.) Liebl.) stands. Scandinavian Journal of Forest Research, 38 (1-2), 87-96.
dc.identifier.doi10.1080/02827581.2023.2168044
dc.identifier.endpage96en_US
dc.identifier.issue1-2en_US
dc.identifier.scopus2-s2.0-85147021304
dc.identifier.startpage87en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1526
dc.identifier.volume38en_US
dc.identifier.wosWOS:000919746200001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.relation.ispartofScandinavian Journal of Forest Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial neural networksen_US
dc.subjectDBHen_US
dc.subjectHeight-diametermodelen_US
dc.subjectRandom foresten_US
dc.titleEstimation of tree height with machine learning techniques in coppice-originated pure sessile oak (Quercus petraea (Matt.) Liebl.) stands
dc.typeArticle

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