Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/90152
Title: High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging
Authors: Sergeev, A. P.
Tarasov, D. A.
Buevich, A. G.
Subbotina, I. E.
Shichkin, A. V.
Sergeeva, M. V.
Lvova, O. A.
Issue Date: 2017
Publisher: American Institute of Physics Inc.
Citation: High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging / A. P. Sergeev, D. A. Tarasov, A. G. Buevich, I. E. Subbotina, et al. . — DOI 10.1063/1.4981963 // AIP Conference Proceedings. — 2017. — Iss. 1836. — 20023.
Abstract: 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).
Keywords: ARTIFICIAL NEURAL NETWORKS
CHROMIUM
GRNNRK
MLPRK
POLLUTION
RESIDUAL KRIGING
URI: http://hdl.handle.net/10995/90152
https://elar.urfu.ru/handle/10995/90152
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85021363916
WOS ID: 000409539000023
PURE ID: 1931633
ISSN: 0094-243X
ISBN: 9780735415065
DOI: 10.1063/1.4981963
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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