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Artificial neural networks as an alternative method to nonlinear mixed-effects models for tree height predictions
Mitja Skudnik, Jernej Jevšenak, 2022, izvirni znanstveni članek

Povzetek: Tree heights are one of the most important aspects of forest mensuration, but data are often unavailable due to costly and time-consuming field measurements. Therefore, various types of models have been developed for the imputation of tree heights for unmeasured trees, with mixed-effects models being one of the most commonly applied approaches. The disadvantage here is the need of sufficient sample size per tree species for each plot, which is often not met, especially in mixed forests. To avoid this limitation, we used principal component analysis (PCA) for the grouping of similar plots based on the most relevant site descriptors. Next, we compared mixed-effects models with height-diameter models based on artificial neural networks (ANN). In terms of root mean square error (RMSE), mixed-effects models provided the most accurate tree height predictions at the plot level, especially for tree species with a smaller number of tree height measurements. When plots were grouped using the PCA and the number of observations per category increased, ANN predictions improved and became more accurate than those provided by mixed-effects models. The performance of ANN also increased when the competition index was included as an additional explanatory variable. Our results show that in the pursuit of the most accurate modelling approach for tree height predictions, ANN should be seriously considered, especially when the number of tree measurements and their distribution is sufficient.
Ključne besede: height-diameter models, national forest inventory, permanent sample plot, mixed forests, model comparison, principal component analysis
Objavljeno v DiRROS: 08.06.2022; Ogledov: 79; Prenosov: 13
URL Povezava na datoteko

Impact of climate change on landslides in Slovenia in the mid-21st century
Mateja Jemec Auflič, Gašper Bokal, Špela Kumelj, Anže Medved, Mojca Dolinar, Jernej Jež, 2021, izvirni znanstveni članek

Povzetek: Slovenia is affected by extreme and intense rainfall that triggers numerous landslides every year, resulting in significant human impact and damage to infrastructure. Previous studies on landslides have shown how rainfall patterns can influence landslide occurrence, while in this paper, we present one of the first study in Slovenia to examine the impact of climate change on landslides in the mid-21st century. To do this, we used the Representative Concentration Pathway (RCP) 4.5 climate scenario and future climatology simulated by six climate models that differed from each other as much as possible while representing measured values of past climate variables as closely as possible. Based on baseline period (1981-2010) we showed the number of days with exceedance of rainfall thresholds and the area where landslides may occur more frequently in the projection period (2041-2070). We found that extreme rainfall events are likely to occur more frequent in the future, which may lead to a higher frequency of landslides in some areas.
Ključne besede: climate change, landslides, models, hazard, prediction
Objavljeno v DiRROS: 09.03.2022; Ogledov: 238; Prenosov: 92
.pdf Celotno besedilo (4,78 MB)

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