Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas.
Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas.
复制标题
矿垦区重金属光谱估算模型构建
DOI：
10.3390/ijerph13070640
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发表时间：
2016-06-28
影响因子：
--
通讯作者：
Li S
中科院分区：
文献类型：
--
作者：
Dong J;Dai W;Xu J;Li S
以徐州柳新矿区表层土壤为研究对象，采用多元线性回归(MLR)、广义回归神经网络(GRNN)和支持向量机序列最小优化(SMO-SVM)方法建立定量模型，对土壤重金属含量和光谱数据进行了研究。研究结果表明：(1)基于MLR、GRNN和SMO-支持向量机建立的光谱反演模型的估计结果令人满意，其中MLR模型的估计效果最差，R2>0.46。这一结果表明重金属污染的应力敏感带包含了足够的有效光谱信息；(2)GRNN模型比MLR模型能更有效地模拟小样本数据，GRNN模型估计的五种重金属含量与实测值之间的R2约为0.7；(3)SMO-SVM模型的谱估计的稳定性和精度明显好于GRNN和MLR模型。在5种重金属中，最小二乘支持向量机模型对Cd的估计值最好，其R2值达到0.8628；(4)利用最优模型对矿区复垦土壤上种植的小麦Cd含量进行了反演，其R2和均方根误差分别为0.6683和0.0489。这一结果表明，利用SMO-支持向量机模型估计小麦样品中重金属含量的方法是可行的。
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.