High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging-Reference-Cited by-同舟云学术

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High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging

Published:2018-01 Issue: Volume:88 Page:188-197
ISSN:0883-2927
Container-title:Applied Geochemistry
language:en
Short-container-title:Applied Geochemistry

Author:

Tarasov D.A.^ORCID,Buevich A.G.^ORCID,Sergeev A.P.^ORCID,Shichkin A.V.^ORCID

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Pollution,Environmental Chemistry

Reference50 articles.

1. Estimation of heavy metal sorption in German soils using artificial neural networks;Anagu;Geoderma,2009

2. Terrain interpolation based on kriging method;Bao;Geogr. Geo-Inform. Sci.,2007

3. Neural Networks for Pattern Recognition;Bishop,1995

4. Field-scale variability of soil properties in central Iowa soils;Cambardella;Soil Sci. Soc. Am. J.,1994

5. Diagnostics of snow-cover contamination with soluble and insoluble metal impurities;Chukanov;Russ. J. Nondestruct. Test.,2006

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