Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region

Time series forecasting is relevant in many fields of human activity. In particular, when studying the processes associated with global warming, such forecasts are very important. The present study used data of the concentration of the greenhouse gases (methane) in the surface layer of atmospheric air on the Arctic island Belyi, Russia. For the work, the time interval of 170 hours (about a week) was chosen during the summer period, characterized by significant daily fluctuations of methane concentration. Models based on artificial neural networks (ANN) such as Nonlinear Autoregressive Neural Network with an External Input (NARX), Elman Neural Network (ENN), and Multi-Layer Perceptron (MLP) were used for modelling. Methane concentrations corresponding to the first 150 hours of the interval used for ANN training, then the concentrations were predicted for the next 20 hours. The model based on the ANN type NARX showed the best accuracy. © 2018 Author(s)

Comparison of artificial neural network, random forest and random perceptron forest for forecasting the spatial impurity distribution

The paper is present a comparison of modern approaches for predicting the spatial distribution in the upper soil layer of a chemical element chromium (Cr), which had spots of anomalously high concentration in the investigated region. The distribution of a normally distributed element copper (Cu) was also predicted. The data were obtained as a result of soil screening in the city of Tarko-Sale, Russia. Models based on artificial neural networks (multilayer perceptron MLP), random forests (RF), and also a model based on a random forest in which MLP used as a tree - a random perceptron forest (RMLPF) - were considered. The models were implemented in MATLAB. Approaches using artificial neural networks (MLP and RMLPF) were significantly more accurate for anomalously distributed Cr. Models based on RF algorithms proved to be more accurate for normally distributed copper. In general, the proposed model RMLPF was the most universal and accurate. © 2018 Author(s)

Features of legal regulation of the process of restoration of retail trade in the initial period of the new economic policy (1921 - 1922)

The article discusses the features of the formation of Soviet legislation on private trade at the initial stage of the "new economic policy" (NEP), which lie in the fact that, despite the possibility for a Soviet private entrepreneur to realize his initiative, from March 1921 to May 1922, positions private capital in Soviet society were not regulated by law, and the legal status of private property was fixed only from January 1, 1923.В статье рассматриваются особенности формирования советского законодательства о частной торговле на начальном этапе «новой экономической политики» (НЭП), которые заключаются в том, что, несмотря на возможность для советского частного предпринимателя реализовать свою инициативу, с марта 1921 г. по май 1922 года позиции частного капитала в советском обществе не были законодательно отрегулированы, а правовой статус частной собственности закреплен только с 1 января 1923 г

Animal and plant world of Vitebsk region as a part of ecological system of Belarus

животные, растения, экологические системы, Витебская область, Беларусь, английский язы

The main sources of formation of supply in the Belarusian market of consumer goods during the period of the "new economic policy" (1921 - 1928)

The New Economic Policy helped restore trade and revitalize the economy. But there was no economic equilibrium in the consumer goods market during the NEP period. This happened due to the fact that in the 1920s. agricultural production in Soviet Belarus mainly supplied the local consumer market. The volume of industrial production in comparison with the pre-war period before 1927 was not fully restored. The bulk of industrial enterprises belonged to the private sector. But the private trader produced only 2% of the total industrial output of the republic. The disruption of transport links led to the fact that the private trader's products did not reach the level of the republican market, but were distributed within their region. The main volume of goods in the 1920s. on the republican consumer market, it was provided by the state sector of industry, the share of which ranged from 3 to 10% of all industrial enterprises of the republic. Government intervention in the work of state-owned enterprises led to the fact that they could not meet the demand of the population.«Новая экономическая политика» способствовала восстановлению торговли и активизации экономики. Но экономического равновесия на рынке потребительских товаров в период нэпа не существовало. Это произошло вследствие того, что в 1920-е гг. сельскохозяйственное производство в советской Беларуси в основном обеспечивало местный потребительский рынок. Объемы промышленного производства в сравнении с довоенным периодом полностью восстановить не удалось. Основная часть промышленных предприятий относилась к частному сектору. Но частник производил только 2% всей промышленной продукции республики. Нарушение транспортных связей привело к тому, что продукция частника не выходила на уровень республиканского рынка, а имела распространение в пределах своего региона. Основной объем товаров в 1920-е гг. на общереспубликанском потребительском рынке давал государственный сектор промышленности, доля которого составляла от 3 до 10% от общего числа промышленных предприятий республики. Вмешательство государства в работу госпредприятий привело к тому, что они не смогли удовлетворять спрос населения. В результате в формировании предложения на потребительском рынке промышленных товаров существенную роль играли импорт и контрабанда

Analytic treatment of nuclear spin-lattice relaxation for diffusion in a cone model

We consider nuclear spin-lattice relaxation rate resulted from a diffusion equation for rotational wobbling in a cone. We show that the widespread point of view that there are no analytical expressions for correlation functions for wobbling in a cone model is invalid and prove that nuclear spin-lattice relaxation in this model is exactly tractable and amenable to full analytical description. The mechanism of relaxation is assumed to be due to dipole-dipole interaction of nuclear spins and is treated within the framework of the standard Bloemberger, Purcell, Pound - Solomon scheme. We consider the general case of arbitrary orientation of the cone axis relative the magnetic field. The BPP-Solomon scheme is shown to remain valid for systems with the distribution of the cone axes depending only on the tilt relative the magnetic field but otherwise being isotropic. We consider the case of random isotropic orientation of cone axes relative the magnetic field taking place in powders. Also we consider the cases of their predominant orientation along or opposite the magnetic field and that of their predominant orientation transverse to the magnetic field which may be relevant for, e.g., liquid crystals. Besides we treat in details the model case of the cone axis directed along the magnetic field. The latter provides direct comparison of the limiting case of our formulas with the textbook formulas for free isotropic rotational diffusion. The dependence of the spin-lattice relaxation rate on the cone half-width yields results similar to those predicted by the model-free approach.Comment: 29 p., 7 fig. arXiv admin note: substantial text overlap with arXiv:1101.249

Training algorithms for artificial neural network in predicting of the content of chemical elements in the upper soil layer

Models based on Artificial Neural Networks (ANN) in recent years are increasingly being used in environmental studies. Among the many types of ANN, the network type Multilayer Perceptron (MLP) has become most widespread. Such networks are universal, simple, and suitable for most tasks. The main problem when modelling using MLP is the choice of the learning algorithm. In this paper, we compared several learning algorithms: Levenberg-Marquart (LM), LM with Bayes regularization (BR), gradient descent (GD), and GD with the speed parameter setting (GDA). The data for modelling were taken from the results of the soil screening of an urbanized area. The spatial distribution of the chemical element Chromium (Cr) in the surface layer of the soil was simulated. The structure of the MLP network was chosen using computer simulations based on minimization of the root mean squared error (RMSE). The model using the LM training algorithm showed the best accuracy. © 2018 Author(s)

Topsoil pollution forecasting using artificial neural networks on the example of the abnormally distributed heavy metal at Russian subarctic

Forecasting the soil pollution is a considerable field of study in the light of the general concern of environmental protection issues. Due to the variation of content and spatial heterogeneity of pollutants distribution at urban areas, the conventional spatial interpolation models implemented in many GIS packages mostly cannot provide appreciate interpolation accuracy. Moreover, the problem of prediction the distribution of the element with high variability in the concentration at the study site is particularly difficult. The work presents two neural networks models forecasting a spatial content of the abnormally distributed soil pollutant (Cr) at a particular location of the subarctic Novy Urengoy, Russia. A method of generalized regression neural network (GRNN) was compared to a common multilayer perceptron (MLP) model. The proposed techniques have been built, implemented and tested using ArcGIS and MATLAB. To verify the models performances, 150 scattered input data points (pollutant concentrations) have been selected from 8.5 km2 area and then split into independent training data set (105 points) and validation data set (45 points). The training data set was generated for the interpolation using ordinary kriging while the validation data set was used to test their accuracies. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. The predictive accuracy of both models was confirmed to be significantly higher than those achieved by the geostatistical approach (kriging). It is shown that MLP could achieve better accuracy than both kriging and even GRNN for interpolating surfaces. © 2017 Author(s)

Statistical analysis of the spatial distribution of impurities in the snow cover in the vicinity of copper mine in the Middle Ural of Russia

Statistical analysis of the monitoring data of industrial enterprises influence zones is an important part of the researches related to natural environment changes. In present study, a cluster analysis of the elemental composition of the snow cover in the vicinity of a copper mine was carried out. The data were obtained as a result of the chemical analysis of the snow samples collected during annual environmental monitoring in the region of Rezh town (the Middle Ural of Russia), where Safyanovsky Copper Mine and Rezhevsky Nickel Plant are located. The elements identified by chemical analysis were grouped according to the strength of the correlation bond. The cluster analysis of these groups made it possible to identify and separate the influence zones of the Plant, Mine and other industrial objects located in the area. The obtained results became the basis for adjusting the snow cover monitoring scheme. © 2018 Author(s).The research is supported by the project of the Ural Branch of Russian Academy of Sciences No. 18-5-2345-56

Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method-kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set. © 2017 Author(s)