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This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems. [ABSTRACT FROM AUTHOR]

Spatiotemporal assessment of solar potential is one of the most promising solutions to find suitable locations for future photovoltaic systems' placement. However, accurate assessment of solar potential on large areas can be challenging due to missing data or computational complexity. In this paper, a fully convolutional neural network based method for high-resolution spatiotemporal assessment of solar potential by using remote sensing data is presented. The method is trained and validated on the area of 32 km2 of the Maribor city, Slovenia, and tested on the 6 different locations in Slovenia and Germany. The proposed method was tested against the simulation algorithm, which utilized isotropic Liu Jordan diffuse model or anisotropic Perez model with sky view factor based shading. On average, a normalized root mean square error (NRMSE) of 6.06% and mean absolute percentage error (MAPE) of 4.29% was achieved in test locations against the simulation based on Liu-Jordan model. When training the fully convolutional neural network models against the ground truth, generated with more advanced Perez diffuse model, the average NRMSE was 8.37% and MAPE of 10.90% was achieved across all test locations. Additionally, it was shown that the proposed method can assess solar potential in high-resolution from the input in lower resolution with higher accuracy than the simulation algorithm, while being up to 1500-times faster. [ABSTRACT FROM AUTHOR]

The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy for soil properties prediction using optical spectroscopy sensors. Several research results have been stated and investigated. A comparison is made between six commonly used techniques: Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine, Least-Square Support Vector Machine and Artificial Neural Network, showing that the best prediction accuracy cannot always be achieved by the most common and complicated method. The influence of the chosen category for nutrient characterization was investigated, indicating better prediction when a multi-component strategy was used. In contrast, the prediction of single-component soil properties was less accurate. In addition, the influence of category levels was not as significant as expected when choosing between 3-level, 5-level or 13-level nutrient characterization for some nutrients, which can be used for a more precise nutrient characterization strategy. A comparative analysis was performed between soil from a local farm with similar texture and soils collected from different locations in Slovenia, which gave a better prediction for a local farm. Finally, the influence of principal component analysis was validated using 5, 10, 20 and 50 first principal components, indicating the better performance of machine learning when using the 50 principal components. [ABSTRACT FROM AUTHOR]

We examined valuable log assortments of Norway spruce (Picea abies) from a traditional auction in Slovenia where spruce growth on many sites is affected by climate change. From 6620 logs, we selected 817 that obtained the highest prices. Factors including log dimensions and geometry, tree-ring characteristics, quality grades according to the standard, properties measured by NDE stress wave testing, and their combined effect on price were modelled. The results showed that half of the auctioned logs were of highest quality (Q1, Q2), with diameters over 60 cm. These logs were more expensive than the thinner logs of lower quality (Q3, Q4). The quality class of the logs, determined by their external features and geometry, was associated with tree-ring and acoustic characteristics. The artificial neural network model (ANN) with feed-forward backpropagation using tree-ring data, longitudinal stress wave velocity, and damping showed that more than 75% of the logs could be accurately classified into quality classes. On the other hand, tree-ring data and acoustic characteristics could not adequately explain the price offered at auction, which probably also depends on unidentified individual requirements and the needs of the buyer. [ABSTRACT FROM AUTHOR]

Transversal competences constitute a set of the knowledge, skills, and attitudes required for various positions and in different professions. Such competences include: entrepreneurship, teamwork, creativity, and communicativeness; they are increasingly listed by employers in different countries as the key requirements in the labor market. The article presents the model of accelerating the process of acquiring transversal competences, developed based on the analysis of data collected in four countries of the European Union: Poland, Finland, Slovakia, and Slovenia. In the analysis, multivariate additive regression spline method was used, along with artificial neural networks, in order to create the best model describing the influence of different variables on the acceleration of acquiring transversal competences. Herewith, we demonstrated that by accelerating the acquisition of the transversal competence of entrepreneurship is influenced by the following factors: rank of the training method in the developed matrix, student numbers and the weighted average of the pace of acceleration regarding the acquisition of the remaining transversal competences, i.e., teamwork, communicativeness and creativity by the given student. The results validate our new method of the acceleration of acquiring transversal competences by students. Students may be from various higher education institutions in different countries. Developed results may be used in the course of education within the framework of the already planned vocational courses and for developing the skills required by employers for various positions and in different professions. [ABSTRACT FROM AUTHOR]

Early prediction of natural disasters like floods and landslides is essential for reasons of public safety. This can be attained by processing Synthetic-Aperture Radar (SAR) images and retrieving soil-moisture parameters. In this article, TerraSAR-X product images are investigated in combination with a water-cloud model based on the Shi semi-empirical model to determine the accuracy of soil-moisture parameter retrieval. SAR images were captured between January 2008 and September 2010 in the vicinity of the city Maribor, Slovenia, at different incidence angles. The water-cloud model provides acceptable estimated soil-moisture parameters at bare or scarcely vegetated soil areas. However, this model is too sensitive to speckle noise; therefore, a pre-processing step for speckle-noise reduction is carried out. Afterwards, self-organizing neural networks (SOM) are used to segment the areas at which the performance of this model is poor, and at the same time neural networks are also used for a more accurate approximation of model parameters' values. Ground-truth is measured using the Pico64 sensor located on the field, simultaneously with capturing SAR images, in order to enable the comparison and validation of the obtained results. Experimental results show that the proposed method outperforms the water-cloud model accuracy over all incidence angles. [ABSTRACT FROM AUTHOR]