Modelling potentially toxic elements in forest soils with viseNIR spectra and learning algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F20%3A81999" target="_blank" >RIV/60460709:41210/20:81999 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S026974912036262X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S026974912036262X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.envpol.2020.115574" target="_blank" >10.1016/j.envpol.2020.115574</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modelling potentially toxic elements in forest soils with viseNIR spectra and learning algorithms

Popis výsledku v původním jazyce

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible-near infrared (vis-NIR 350-2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis-NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis-NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and

Název v anglickém jazyce

Modelling potentially toxic elements in forest soils with viseNIR spectra and learning algorithms

Popis výsledku anglicky

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible-near infrared (vis-NIR 350-2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis-NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis-NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

<a href="/cs/project/GJ18-28126Y" target="_blank" >GJ18-28126Y: Hodnocení kontaminace půdy s využitím hyperspektrálních satelitních dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Environmental Pollution

ISSN

0269-7491

e-ISSN

1873-6424

Svazek periodika

267

Číslo periodika v rámci svazku

december

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

0-0

Kód UT WoS článku

000593966000003

EID výsledku v databázi Scopus

2-s2.0-85090718445