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1.
Evaluation of thermal conductivity estimation models with laboratory-measured thermal conductivities of sediments
Simona Adrinek, Rao Martand Singh, Mitja Janža, Mateusz Żeruń, Grzegorz Ryżyński, 2022, original scientific article

Abstract: Thermal conductivity is one of the key parameters for estimating low-temperature geothermal potential. In addition to field techniques, it can be determined based on physical parameters of the sediment measured in the laboratory. Following the methodology for cohesive and non-cohesive sample preparation, laboratory measurements were carried out on 30 samples of sediments. Density, porosity and water content of samples were measured and used in thermal conductivity estimation models (TCEM). The bulk thermal conductivity (λb) calculated with six TCEMs was compared with the measured λb to evaluate the predictive capacity of the analytical methods used. The results show that the empirical TCEMs are suitable to predict the λb of the analysed sediment types, with the standard deviation of the residuals (RMSE) ranging from 0.11 to 0.35 Wm−1 K−1. To improve the fit, this study provides a new modified parameterisation of two empirical TCEMs (Kersten and Côté&Konrad model) and, therefore, suggests the most suitable TCEMs for specific sample conditions. The RMSE ranges from 0.11 to 0.29 Wm−1 K−1. Mixing TCEM showed an RMSE of up to 2.00 Wm−1 K−1, meaning they are not suitable for predicting sediment λb. The study provides an insight into the analytical determination of thermal conductivity based on the physical properties of sediments. The results can help to estimate the low-temperature geothermal potential more quickly and easily and promote the sustainable use of this renewable energy source, which has applications in environmental and engineering science.
Keywords: thermal conductivity, non-cohesive sediment, cohesive sediment, estimation model
Published in DiRROS: 25.08.2022; Views: 83; Downloads: 51
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2.
Modelling seasonal dynamics of secondary growth in R
Jernej Jevšenak, Jožica Gričar, Sergio Rossi, Peter Prislan, 2022, original scientific article

Abstract: The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.
Keywords: artificial neural networks, cambium, generalized additive model, Gompertz function, growing season, intra-annual time series
Published in DiRROS: 21.07.2022; Views: 102; Downloads: 85
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3.
Synoptic risk assessment of groundwater contamination from landfills
Sonja Cerar, Luka Serianz, Katja Koren, Joerg Prestor, Nina Mali, 2022, original scientific article

Abstract: Waste management in Europe has improved in recent years, reducing the amount of waste disposed at landfills. However, there are still many landfills in the countries. It is well known that landfills that do not have measures in place to control leachate entering groundwater can contaminate groundwater long after the landfill is closed. Collecting monitoring results from all landfills allows permitting and management agencies to improve action plans. This relies on a synoptic risk assessment that allows prioritization and milestones to be set for required actions. The developed method of synoptic risk assessment is based on a conceptual model of the landfill and the results of chemical groundwater monitoring tested at 69 landfills in Slovenia. The study confirms that most landfills have a direct or indirect impact on groundwater quality. All landfills were classified into three priority classes on the basis of the synoptic risk assessment. The results show that a total of 24 landfills have a clearly pronounced impact on groundwater. A total of 31 landfills have a less pronounced impact due to the favorable natural attenuation capacity of the soil or the technically appropriate design of the landfill itself. A total of 14 landfills have a less pronounced or negligible impact on groundwater.
Keywords: conceptual model, synoptic risk assessment, landfill, groundwater, chemical analysis
Published in DiRROS: 19.07.2022; Views: 133; Downloads: 91
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4.
Adsorption of lead from contaminated water using biosorbent
M. Dharsana, J. Prakash Arul Jose, 2022, original scientific article

Keywords: biosorbents, isotherm model, lead, Langmuir, removal
Published in DiRROS: 22.06.2022; Views: 77; Downloads: 21
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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, original scientific article

Abstract: 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.
Keywords: height-diameter models, national forest inventory, permanent sample plot, mixed forests, model comparison, principal component analysis
Published in DiRROS: 08.06.2022; Views: 128; Downloads: 48
URL Link to file

8.
Copper wire, model and equation of electrical resistance
Franc Vodopivec, 2021, short scientific article

Keywords: copper wire, model, electrical resistance, number of atoms, oxygen atoms
Published in DiRROS: 06.05.2022; Views: 111; Downloads: 0

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