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Query: "author" (Sašo Džeroski) .

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1.
Uporaba metod strojnega učenja za preučevanje odnosov med značilnostmi branik in okoljem
Jernej Jevšenak, Sašo Džeroski, Tom Levanič, 2017, original scientific article

Abstract: 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.
Keywords: strojno učenje, primerjava metod, dendroklimatologija, umetne nevronske mreže, modelna drevesa, ansambel modelnih dreves, naključni gozdovi, linearna regresija
Published in DiRROS: 21.02.2018; Views: 3524; Downloads: 2167
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2.
Windthrow factors - a case study on Pokljuka
Nikica Ogris, Sašo Džeroski, Maja Jurc, 2004, original scientific article

Abstract: This paper presents a case study in windthrow. The case study area was 1.7 ha of two forest gaps on the Pokljuka plateau, Slovenia, where strong wind had blown down 44 trees. An additional 44 standing trees closest to the fallen trees were used as a control group for comparative purposes. The following variables were measured for fallen trees: breast diameter, height, crown diameter and height as well, the number and diameter of roots, the volume of the root system, and root rot. Standing trees were measured for breast diameter, height, crown diameter and height, and the number and diameter of roots. The data were analysed using the machine learning methods in the Weka computer program. The most important factors of windthrow in the case study area were: storm wind (speed above 17 m/s), wet shallow soil, and the edges ofthe forest gaps. The results of the case study show that breast diameter, tree height and the presence of root rot can be classified as windthrow factors.
Keywords: wind, windthrow, root rot, factors of windthrow
Published in DiRROS: 12.07.2017; Views: 2790; Downloads: 1202
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