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)

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)

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)

High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging

The work deals with the application of neural networks residual kriging (NNRK) to the spatial prediction of the abnormally distributed soil pollutant (Cr). It is known that combination of geostatistical interpolation approaches (kriging) and neural networks leads to significantly better prediction accuracy and productivity. Generalized regression neural networks and multilayer perceptrons are classes of neural networks widely used for the continuous function mapping. Each network has its own pros and cons; however both demonstrated fast training and good mapping possibilities. In the work, we examined and compared two combined techniques: generalized regression neural network residual kriging (GRNNRK) and multilayer perceptron residual kriging (MLPRK). The case study is based on the real data sets on surface contamination by chromium at a particular location of the subarctic Novy Urengoy, Russia, obtained during the previously conducted screening. The proposed models have been built, implemented and validated using ArcGIS and MATLAB environments. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. MLRPK showed the best predictive accuracy comparing to the geostatistical approach (kriging) and even to GRNNRK. © 2017 Author(s)

Data separation for training the artificial neural network to simulate the spatial distribution of chromium in the surface layer of the soil

An algorithm for dividing data into training and test subsamples to simulate the spatial distribution of chromium in the surface layer of the soil using artificial neural networks (ANN) was proposed. The algorithm takes into account the spatial inhomogeneity of the variable being modelled. The data was obtained during the soil screening on the urbanized area in Novyy Urengoy city. A model, which used controlled separation, had shown more accurate results

Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area

The study is based on the data obtained as a result of soil screening in the city of Noyabrsk, Russia. A comparison of two types of neural networks most commonly used in this type of research was carried out: multi-layer perceptron (MLP), generalized regression neural network (GRNN), and a combined MLP and ordinary kriging approach (MLPRK) for predicting the spatial distribution of the chemical element Chromium (Cr) in the surface layer of the urbanized territory. The model structures were developed using computer modeling, based on minimizing of a root mean squared error (RMSE). As input parameters, the spatial coordinates were used, and the concentration of Cr - as the output. The hybrid MLPRK approach showed the best prognostic accuracy. © 2018 Author(s)

Двухшаговый комбинированный алгоритм повышения точности прогнозирования концентрации метана в атмосферном воздухе на основе нейронной сети NARX и последующего прогнозирования невязок

Climate change in the Arctic is great and can have a significant inverse effect on the global climate, which determines the global significance of climate change in the Arctic. To date, many issues regarding the mechanisms responsible for the rapid melting of Arctic ice and permafrost degradation have not been resolved. It is not known when and what consequences these changes will lead to. Assessing the relationship between global warming and greenhouse gas emissions is an important environmental challenge. Among the main greenhouse gases, the evolution and climate-forming role of the carbon dioxide have been studied. The data on the methane subcycle of the carbon cycle is much less. In the paper, the authors propose a two-step combined algorithm (NARXR) to improve the accuracy of predicting methane concentration in atmospheric air based on the NARX neural network and subsequent prediction of the residuals. Two commonly used models based on artificial neural networks (ANN) for predicting time series are compared to determine the most appropriate base model. Nonlinear autoregressive neural network with external input (NARX) and Elman’s neural network are used. For the forecast, the authors use data on the methane concentration (CH4) in the atmospheric surface layer on the Arctic Island of Bely (Russia). Data is selected for a time interval of 192 hours, because it is characterized by significant daily fluctuations in the concentration of CH4. Values corresponding to the first 168 hours of the interval are used to train the ANN, and then concentrations are predicted for the next 24 hours. The proposed approach shows more accurate forecast results. © Subbotina I. E., Buevich A. G., Sergeev A. P., Shichkin A. V., Baglaeva E. M., Remezova M. S., 2020.The authors are grateful to the Department of Science and Innovation of the Yamal-Nenets Autonomous District and to the NP Russian Center for the Development of the Arctic, city of Salekhard, for technical and logistical support of scientific expeditions to the Island of Bely. The authors also thank the reviewers for constructive criticism and useful recommendations that have improved the quality of article materials

Resonant nonlinear magneto-optical effects in atoms

In this article, we review the history, current status, physical mechanisms, experimental methods, and applications of nonlinear magneto-optical effects in atomic vapors. We begin by describing the pioneering work of Macaluso and Corbino over a century ago on linear magneto-optical effects (in which the properties of the medium do not depend on the light power) in the vicinity of atomic resonances, and contrast these effects with various nonlinear magneto-optical phenomena that have been studied both theoretically and experimentally since the late 1960s. In recent years, the field of nonlinear magneto-optics has experienced a revival of interest that has led to a number of developments, including the observation of ultra-narrow (1-Hz) magneto-optical resonances, applications in sensitive magnetometry, nonlinear magneto-optical tomography, and the possibility of a search for parity- and time-reversal-invariance violation in atoms.Comment: 51 pages, 23 figures, to appear in Rev. Mod. Phys. in Oct. 2002, Figure added, typos corrected, text edited for clarit

Проблема выбора тактики ведения пациентов с высоким и очень высоким риском рака предстательной железы: обзор литературы

Prostate cancer is the largest problem among the male population of the planet, despite the slow development of the disease, thousands of men die from this disease every year. Taking into account the following facts, we can say about the importance and relevance of this problem. This review highlights the results of different treatment strategies for high and very high risk prostate cancer.Рак предстательной железы является серьезной проблемой среди мужского населения планеты. Несмотря на медленное развитие заболевания, каждый год от этой патологии умирают тысячи мужчин. Поэтому лечение рака предстательной железы остается важным и актуальным вопросом. В настоящем обзоре освещены результаты различных стратегий лечения рака предстательной железы высокого и очень высокого риска.