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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Modelling potentially toxic elements in forest soils with viseNIR spectra and learning algorithms
Original language description
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
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
40104 - Soil science

Project
<a href="/en/project/GJ18-28126Y" target="_blank" >GJ18-28126Y: Soil contamination assessment using hyperspectral orbital data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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