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Iskalni niz: "avtor" (Sašo Džeroski) .

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1.
Windthrow factors - a case study on Pokljuka
Nikica Ogris, Sašo Džeroski, Maja Jurc, 2004

Povzetek: 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.
Ključne besede: wind, windthrow, root rot, factors of windthrow
DiRROS - Objavljeno: 12.07.2017; Ogledov: 1086; Prenosov: 269
URL Celotno besedilo (0,00 KB)
Gradivo ima več datotek! Več...

2.
Uporaba metod strojnega učenja za preučevanje odnosov med značilnostmi branik in okoljem
Tom Levanič, Sašo Džeroski, Jernej Jevšenak, 2017

Povzetek: 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.
Ključne besede: strojno učenje, primerjava metod, dendroklimatologija, umetne nevronske mreže, modelna drevesa, ansambel modelnih dreves, naključni gozdovi, linearna regresija
DiRROS - Objavljeno: 21.02.2018; Ogledov: 1353; Prenosov: 437
.pdf Celotno besedilo (1,18 MB)

3.
A machine learning approach to analyzing the relationship between temperatures and multi-proxy tree-ring records
Jernej Jevšenak, Sašo Džeroski, Saša Zavadlav, Tom Levanič, 2018

Povzetek: Machine learning (ML) is a widely unexplored field in dendroclimatology, but it is a powerful tool that might improve the accuracy of climate reconstructions. In this paper, different ML algorithms are compared to climate reconstruction from tree-ring proxies. The algorithms considered are multiple linear regression (MLR), artificial neural networks (ANN), model trees (MT), bagging of model trees (BMT), and random forests of regression trees (RF). April-May mean temperature at a Quercus robur stand in Slovenia is predicted with mean vessel area (MVA, correlation coefficient with April-May mean temperature, r = 0.70, p < 0.001) and earlywood width (EW, r = %0.28, p < 0.05). Similarly, June-August mean temperature is predicted with stable carbon isotope (%13C, r = 0.72, p < 0.001), stable oxygen isotope (%18O, r = 0.32, p < 0.05) and tree-ring width (TRW, r = 0.11, p > 0.05 (ns)) chronologies. The predictive performance of ML algorithms was estimated by 3-fold cross-validation repeated 100 times. In both spring and summer temperature models, BMT performed best respectively in 62% and 52% of the 100 repetitions. The second-best method was ANN. Although BMT gave the best validation results, the differences in the models% performances were minor. We therefore recommend always comparing different ML regression techniques and selecting the optimal one for applications in dendroclimatology.
Ključne besede: dendroclimatology, machine learning, artificial neural networks, model trees, multiple linear regression
DiRROS - Objavljeno: 20.02.2020; Ogledov: 153; Prenosov: 97
URL Celotno besedilo (0,00 KB)

4.
Comparison of an optimal regression method for climate reconstruction with the compare_methods() function from the dendroTools R package
Jernej Jevšenak, Tom Levanič, Sašo Džeroski, 2018

Povzetek: The selection of a regression technique for climate reconstruction may have an important impact on reconstructed values. In this paper, we introduce the compare_methods() function from the dendroTools R package. This function compares different regression algorithms and returns validation results for each. In addition to mean validation metrics and ranks derived from these, transfer functions should have a key role in the evaluation of different regression algorithms. These are also returned as the output of compare_methods(). Our methodology is introduced on two case studies, one using a mean vessel area (MVA) chronology and one using a standardised tree-ring width (TRW) chronology. The nonlinear machine learning methods compared in our study provided relatively small (if any) improvements in terms of explaining climatic variance. However, they do offer different treatments of extreme values, and if providing more plausible climate reconstructions, this could make them a useful tool for climate reconstruction. We propose the use of the compare_methods() function as a standard methodological check before performing climate reconstruction.
Ključne besede: regression, method comparison, artificial neural networks, model trees, bagging, linear regression, R package
DiRROS - Objavljeno: 20.02.2020; Ogledov: 149; Prenosov: 74
URL Celotno besedilo (0,00 KB)

5.
Predicting the vessel lumen area tree-ring parameter of Quercus robur with linear and nonlinear machine learning algorithms
Jernej Jevšenak, Sašo Džeroski, Tom Levanič, 2018

Povzetek: Climate-growth relationships in Quercus robur chronologies for vessel lumen area (VLA) from two oak stands (QURO-1 and QURO-2) showed a consistent temperature signal: VLA is highly correlated with mean April temperature and the temperature at the end of the previous growing season. QURO-1 showed significant negative correlations with winter sums of precipitation. Selected climate variables were used as predictors of VLA in a comparison of various linear and nonlinear machine learning methods: Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), Model Trees (MT), Bagging of Model Trees (BMT) and Random Forests of Regression Trees (RF). ANN outperformed all the other regression algorithms at both sites. Good performance also characterised RF and BMT, while MLR, and especially MT, displayed weaker performance. Based on our results, advanced machine learning algorithms should be seriously considered in future climate reconstructions
Ključne besede: dendroclimatology, artificial neural networks, multiple linear regression, machine learning, vessel lumen area, Quercus robur
DiRROS - Objavljeno: 20.02.2020; Ogledov: 165; Prenosov: 96
URL Celotno besedilo (0,00 KB)

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