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

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)
Original languageEnglish
Pages (from-to)188-197
Number of pages10
JournalApplied Geochemistry
Volume88
DOIs
Publication statusPublished - 1 Jan 2018

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kriging
artificial neural network
topsoil
Multilayer neural networks
Pollution
Neural networks
pollution
Mean square error
Chromium
chromium
Chemical elements
environmental modeling
chemical element
Contamination
prediction
computer simulation
Computer simulation
anomaly

Keywords

  • Artificial neural networks
  • Chromium
  • Combined modeling
  • GRNNRK
  • MLPRK

ASJC Scopus subject areas

  • Environmental Chemistry
  • Pollution
  • Geochemistry and Petrology

WoS ResearchAreas Categories

  • Geochemistry & Geophysics

Cite this

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title = "High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging",
keywords = "Artificial neural networks, Chromium, Combined modeling, GRNNRK, MLPRK",
author = "Tarasov, {D. A.} and Buevich, {A. G.} and Sergeev, {A. P.} and Shichkin, {A. V.}",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/j.apgeochem.2017.07.007",
language = "English",
volume = "88",
pages = "188--197",
journal = "Applied Geochemistry",
issn = "0883-2927",
publisher = "Elsevier",

}

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T1 - High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging

AU - Tarasov, D. A.

AU - Buevich, A. G.

AU - Sergeev, A. P.

AU - Shichkin, A. V.

PY - 2018/1/1

Y1 - 2018/1/1

KW - Artificial neural networks

KW - Chromium

KW - Combined modeling

KW - GRNNRK

KW - MLPRK

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U2 - 10.1016/j.apgeochem.2017.07.007

DO - 10.1016/j.apgeochem.2017.07.007

M3 - Article

VL - 88

SP - 188

EP - 197

JO - Applied Geochemistry

JF - Applied Geochemistry

SN - 0883-2927

ER -