Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas

Jihong Dong, Wenting Dai, Jiren Xu and Songnian Li
Additional contact information
Jihong Dong: School of Environment Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China
Wenting Dai: School of Environment Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China
Jiren Xu: School of Geography, University of Leeds, Leeds LS2 9JT, UK
Songnian Li: School of Environment Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China

IJERPH, 2016, vol. 13, issue 7, 1-18

Abstract: The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as follows: (1) the estimations of the spectral inversion models established based on MLR, GRNN and SMO-SVM are satisfactory, and the MLR model provides the worst estimation, with R 2 of more than 0.46. This result suggests that the stress sensitive bands of heavy metal pollution contain enough effective spectral information; (2) the GRNN model can simulate the data from small samples more effectively than the MLR model, and the R 2 between the contents of the five heavy metals estimated by the GRNN model and the measured values are approximately 0.7; (3) the stability and accuracy of the spectral estimation using the SMO-SVM model are obviously better than that of the GRNN and MLR models. Among all five types of heavy metals, the estimation for cadmium (Cd) is the best when using the SMO-SVM model, and its R 2 value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R 2 and RMSE between the measured and the estimated values are 0.6683 and 0.0489, respectively. This result suggests that the method using the SMO-SVM model to estimate the contents of heavy metals in wheat samples is feasible.

Keywords: mining area; reclamation soil; heavy metal; spectrum; estimation model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1660-4601/13/7/640/pdf (application/pdf)
https://www.mdpi.com/1660-4601/13/7/640/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:13:y:2016:i:7:p:640-:d:72883

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().