Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging / Sergeev A.P., Tarasov D.A., Buevich A.G., Shichkin A.V., Tyagunov A.G., Medvedev A.N. // AIP Conference Proceedings. - 2017. - V. 1836, l. .

ISSN:
0094243X
Type:
Conference Paper
Abstract:
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).
Author keywords:
Artificial Neural Networks; Kriging; Modeling; Snow Cover; Spatial Distribution
Index keywords:
нет данных
DOI:
10.1063/1.4981973
Смотреть в Scopus:
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021324104&doi=10.1063%2f1.4981973&partnerID=40&md5=58b219a67d9ed98bad06589c244efbe6
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Art. No. 020033
Link https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021324104&doi=10.1063%2f1.4981973&partnerID=40&md5=58b219a67d9ed98bad06589c244efbe6
Affiliations Ural Federal University, Mira, 19, Ekaterinburg, Russian Federation; Institute of Industrial Ecology UB RAS, Sophy Kovalevskoy, 20, Ekaterinburg, Russian Federation
Author Keywords Artificial Neural Networks; Kriging; Modeling; Snow Cover; Spatial Distribution
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Correspondence Address Sergeev, A.P.; Ural Federal University, Mira, 19, Russian Federation; email: alexanderpsergeev@gmail.com
Editors Ntalianis K.
Publisher American Institute of Physics Inc.
Conference name 1st International Conference on Applied Mathematics and Computer Science, ICAMCS 2017
Conference date 27 January 2017 through 29 January 2017
Conference code 128337
ISBN 9780735415065
Language of Original Document English
Abbreviated Source Title AIP Conf. Proc.
Source Scopus