Abstract
In order to limit pollution risk and develop proper remediation strategies, soil quality has to be controlled by rapid and sustainable monitoring measures. Visible and near-infrared reflectance spectroscopy (VisNIR) is an attractive surrogate to time-consuming and costly classical soil assessment protocols. It highly depends on selecting appropriate data mining methods for regression analysis. In this study, performance of a state of the art learning algorithm called extreme learning machine (ELM), was evaluated through comparing with the other calibration methods proposed in the literature for predicting lead (Pb) and Zinc (Zn) concentrations. Solid samples collected from a mine waste dump (n=120) were scanned using a Fieldspec3 portable spectroradiometer with a measurement range of (350–2500nm) in a laboratory. Transformation of the reflectance spectra to absorbance was followed by three pre-processing scenarios including Savitzky-Golay smoothing (SG), first derivative (FD) and second derivative (SD). Partial Least Square Regression (PLSR), Support Vector Machine (SVM) and neural networks with two learning algorithms models (back propagation and extreme learning machine), were calibrated on spectral features selected by genetic algorithm, and then applied to predict soil metal concentrations. The best prediction accuracy was obtained by FD-ELM method with R2p, RMSEp, concordance correlation coefficient and RPD values of 0.93, 63.01, 0.98 and 5.92 for Pb and 0.87, 167.90, 0.91 and 5.62 for Zn, respectively. Study of the prediction mechanism proved that element sorption by spectrally active Fe-oxide and clay contents of the soil was the major mechanism by which the spectrally featureless Pb and Zn ions can be predicted. The spatial patterns of predicted toxic elements showed that FD-ELM had the most similarity with those maps obtained by interpolating measured values. Over all, it is concluded that reflectance spectroscopy combined with the ELM algorithm is a rapid, inexpensive and accurate tool for indirect evaluation of Pb and Zn and mapping their spatial distribution in dumpsite soils of Sarcheshmeh copper mine.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.