title: Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas.
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 R² 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 R² 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 R² value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R² 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.
authors.affiliation: School of Environment Science and Spatial Informatics, China University of Mining &amp; Technology, Xuzhou 221116, China. dongjihong@cumt.edu.cn.|Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. dongjihong@cumt.edu.cn.
authors.forename: Jihong
authors.initials: J
authors.lastname: Dong
authors.affiliation: School of Environment Science and Spatial Informatics, China University of Mining &amp; Technology, Xuzhou 221116, China. daiwentingcumt@126.com.|Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining &amp; Technology, Xuzhou 221116, China. daiwentingcumt@126.com.
authors.forename: Wenting
authors.initials: W
authors.lastname: Dai
authors.affiliation: School of Geography, University of Leeds, Leeds LS2 9JT, UK. jirenxu@126.com.
authors.forename: Jiren
authors.initials: J
authors.lastname: Xu
authors.affiliation: School of Environment Science and Spatial Informatics, China University of Mining &amp; Technology, Xuzhou 221116, China. snli@ryerson.ca.|Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada. snli@ryerson.ca.
authors.forename: Songnian
authors.initials: S
authors.lastname: Li
mesh_terms: D002104:Cadmium / Q000032:analysis; D002681:China; D003247:Conservation of Natural Resources; D004784:Environmental Monitoring / Q000379:methods*; D004785:Environmental Pollutants / Q000032:analysis*; D019216:Metals, Heavy / Q000032:analysis*; D008906:Mining; D008962:Models, Theoretical; D058998:Remote Sensing Technology / Q000379:methods*; D012989:Soil Pollutants / Q000032:analysis*; D014908:Triticum / Q000737:chemistry
chemical_list: D004785:Environmental Pollutants; D019216:Metals, Heavy; D012989:Soil Pollutants; D002104:Cadmium
keywords: estimation model; heavy metal; mining area; reclamation soil; spectrum