Identifying mining-induced chromium contamination in soil through visible-near infrared spectroscopy and machine learning  

作　　者：Chongchong Qi Mengting Wu Kechao Li Tao Hu Danial Jahed Armaghani Qiusong Chen Erol Yilmaz 

机构地区：[1]School of Resources and Safety Engineering,Central South University,Changsha 410083,China [2]School of Metalurgy and Environment,Central South University,Changsha 410083,China [3]Schol of Civil and Environmental Engineering,University of Technology Sydney,Sydney,NSW 2007,Australia [4]Deparment of Civil Engineering,Goechnical Division,Rep Tayp Erdogan Univer,Fener,Rize TR530,Turky

出　　处：《Green and Smart Mining Engineering》2024年第2期132-139,共8页绿色与智能矿业工程（英文）

基　　金：financially supported by the National Natural Science Foundation of China (Nos.22376221 and 52274151);Natural Science Foundation of Hunan Province,China (No.2024JJ2074);Young Elite Scientists Sponsorship Program by CAST (No.2023QNRC001);Unveiling and Commanding Project from Fankou Lead-Zinc Mine;supported in part by the High Performance Computing Center of Central South University。

摘　　要：The extraction,purification,and utilization of mineral resources have been among the largest anthropogenic sources of chromium(Cr)in soil.Determining Cr contamination in soil is a key issue prior to its appropriate remediation.Nevertheless,the efficient identification of large-scale soil Cr contamination requires continuous research.The present study proposes a continental-scale method to rapidly identify soil Cr contamination using visible-near infrared spectroscopy(vis-NIR)and machine learning(ML).A large dataset containing 18,675topsoil samples from the Land Use/Land Cover Area Frame Survey 2009 projects across Europe was compiled.Five advanced ML algorithms were compared,and hyperparameter optimization was conducted using the grid search method.Permutation importance was employed to calculate the rank of each spectral wavelength,shedding light on the most sensitive spectral wavelength for Cr contamination.Results indicate that hyperparameter optimization had the most significant performance improvement on support vector machine(SVM),exhibiting an increase in training performance from 0.795 to 0.868.The achieved optimal SVM accuracy,area under the receiver operating feature curve,sensitivity,and specificity of 0.78,0.85,0.85,and 0.66,respectively,indicating excellent predictive performance on the Cr contamination classification.The optimal SVM model revealed that the most important spectral band for classifying Cr contamination was 1430-1433 nm.This finding implies that the adsorption of molecular water was closely related to the classification of Cr contamination.The current study introduces the first continental-scale identification of Cr contamination using visNIR,which has excellent guiding significance for Cr remediation and the identification of other heavy metals using vis-NIR.

关 键 词：Mining contamination HYPERSPECTRAL Classification Support vector machine Continental scale 

分 类 号：O657.33[理学—分析化学] TP181[理学—化学] X53[自动化与计算机技术—控制理论与控制工程]