Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F21%3A84641" target="_blank" >RIV/60460709:41210/21:84641 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0375674220306403" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0375674220306403</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.gexplo.2020.106680" target="_blank" >10.1016/j.gexplo.2020.106680</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils

Popis výsledku v původním jazyce

The application of multivariate geostatistical and statistical methods remain valuable tools for environmental pollution assessment. In particular, stochastic simulation techniques like sequential Gaussian simulation SGS and the self organizing map artificial neural networks SeOM ANNs have facilitated the understanding of the spatial distribution of potentially toxic elements PTEs in polluted soils. However, there is a dearth of literature on the application of SGS and SeOM ANN in mapping potentially toxic elements PTE in heavily polluted mining and smelter affected floodplain soils. This study shows the applicability SGS and SeOM ANN which is a powerful visualization tool for the categorization of PTEs Cadmium Cd, Arsenic As, Antimony Sb, Lead Pb and Zinc Zn levels together with selected soil properties oxidizable carbon Cox and soil reaction pH H2O in one of the most polluted mining floodplain soils in Europe. A k means algorithm was used to classify distinct clusters which were visually unclear ba

Název v anglickém jazyce

Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils

Popis výsledku anglicky

The application of multivariate geostatistical and statistical methods remain valuable tools for environmental pollution assessment. In particular, stochastic simulation techniques like sequential Gaussian simulation SGS and the self organizing map artificial neural networks SeOM ANNs have facilitated the understanding of the spatial distribution of potentially toxic elements PTEs in polluted soils. However, there is a dearth of literature on the application of SGS and SeOM ANN in mapping potentially toxic elements PTE in heavily polluted mining and smelter affected floodplain soils. This study shows the applicability SGS and SeOM ANN which is a powerful visualization tool for the categorization of PTEs Cadmium Cd, Arsenic As, Antimony Sb, Lead Pb and Zinc Zn levels together with selected soil properties oxidizable carbon Cox and soil reaction pH H2O in one of the most polluted mining floodplain soils in Europe. A k means algorithm was used to classify distinct clusters which were visually unclear ba

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Geochemical Exploration

ISSN

0375-6742

e-ISSN

1879-1689

Svazek periodika

222

Číslo periodika v rámci svazku

mar

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

0-0

Kód UT WoS článku

000612235100003

EID výsledku v databázi Scopus

2-s2.0-85096953213