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Modelling potentially toxic elements in forest soils with vis–NIR spectra and learning algorithms by Asa Gholizadeh; Mohammadmehdi Saberioon; Eyal Ben-Dor; Raphael A. Viscarra Rossel; Luboš Borůvka is a Environmental Science article available to read on EtoBox.

What is Modelling potentially toxic elements in forest soils with vis–NIR spectra and learning algorithms about?

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 humus (H) organic layers. The content of all PTEs was higher in horizon H compared to F horizon. Our results indicate that the reflectance of samples tended to decrease with increased

Who reads Modelling potentially toxic elements in forest soils with vis–NIR spectra and learning algorithms?

Author

Asa Gholizadeh; Mohammadmehdi Saberioon; Eyal Ben-Dor; Raphael A. Viscarra Rossel; Luboš Borůvka

Publisher

Elsevier BV

Published

2020

Language

EN

Field

Environmental Science (Physical Sciences)