20.500.12556/DiRROS-8175 Uporaba metod strojnega učenja za preučevanje odnosov med značilnostmi branik in okoljem On the use of machine learning methods to study the relationships between tree-ring characteristics and the environment Različne študije so pokazale, da lahko z nelinearnimi metodami bolje opišemo (modeliramo) odnos med branikami in okoljem. V naši študiji smo primerjali (multiplo) linearno regresijo (MLR) in štiri nelinearne metode strojnega učenja: modelna drevesa (MT), ansambel bagging modelnih dreves (BMT), umetne nevronske mreže (ANN) in metodo naključnih gozdov (RF). Za primerjavo teh metod modeliranja smo uporabili štiri množice podatkov. Natančnost naučenih modelov smo ocenili z metodo 10-kratnega prečnega preverjanja (ang. 10-fold cross-validation) na naši množici in preverjanjem na dodatni testni množici. Na vseh množicah smo dobili boljše statistične kazalce za nelinearne metode s področja strojnega učenja, s katerimi lahko pojasnimo večji delež variance oz. dobimo manjšo napako. Nobena metoda se ni pokazala kot najboljša v vseh primerih, zato je smiselno predhodno primerjati več različnih metod in nato uporabiti najprimernejšo, npr. za rekonstrukcijo klime. Many studies have shown that by using nonlinear methods, the relationship between tree-ring parameters and the environment can be described (modelled) better and in more detail. In our study, (multiple) linear regression (MLR) with four nonlinear machine learning methods are compared: artificial neural networks (ANN), model trees (MT), bagging of model trees (BMT) and random forests of regression trees (RF). To compare the different regression methods, four datasets were used. The performance of the learned models was estimated by using 10-fold cross-validation and an additional hold-out test. For all datasets, better results were obtained by the nonlinear machine learning regression methods, which can explain more variance and yield lower error. However, none of the considered methods outperformed all other methods for all datasets. Therefore, we suggest testing several different methods before selecting the best one, e.g. for climate reconstruction. strojno učenje primerjava metod dendroklimatologija umetne nevronske mreže modelna drevesa ansambel modelnih dreves naključni gozdovi linearna regresija machine learning method comparison dendroclimatology artificial neural networks model trees ensembles of model trees random forest linear regression true false true Slovenski jezik Angleški jezik Delo ni kategorizirano 2018-02-21 10:07:11 2018-02-21 10:11:13 2022-08-16 09:58:03 0000-00-00 00:00:00 2017 0 0 Besedilo v slov.; str. 21-29 Vol. 114 dec. 2017 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 630*52:630*11(045)=163.6 2335-3112 10.20315/ASetL.114.2 4998310 266761216 ASetL_114_2_Jernej_JEVSENAKl.pdf ASetL_114_2_Jernej_JEVSENAKl.pdf 1 E97089E7EF1BB6098079AF533D74D48C 73fe68e9b67b6befeb4cf6443206478eb460634d67b6e581926b19d7c2c58a42 458c4684-17b5-11ed-b6b8-001a4af901a5 20.500.12556/dirros/08b5e5d9-4507-43f2-947e-1d470da2a673 https://dirros.openscience.si/Dokument.php?lang=slv&id=10065 https://doi.org/10.20315/ASetL.114.2 1 https://dirros.openscience.si/Dokument.php?lang=slv&id=10064 Gozdarski inštitut Slovenije Acta Silvae et Ligni 0 0 0