Dong Jihong, Dai Wenting, Xu Jiren, Li Songnian
School of Environment Science and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China.
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining & Technology, Xuzhou 221116, China.
Int J Environ Res Public Health. 2016 Jun 28;13(7):640. doi: 10.3390/ijerph13070640.
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.
本文报道的研究以徐州刘新矿区的表层土壤为研究对象，通过多元线性回归（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，探究重金属含量与光谱数据。研究结果如下：（1）基于MLR、GRNN和SMO - SVM建立的光谱反演模型估计效果良好，其中MLR模型估计效果最差，R²大于0.46。这表明重金属污染的胁迫敏感波段包含足够有效的光谱信息；（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测量值之间的R²约为0.7；（3）SMO - SVM模型光谱估计的稳定性和准确性明显优于GRNN和MLR模型。在所有五种重金属中，使用SMO - SVM模型对镉（Cd）的估计效果最佳，其R²值达到0.8628；（4）利用最优模型反演复垦土壤种植小麦中的镉含量，测量值与估计值之间的R²和RMSE分别为0.6683和0.0489。这表明使用SMO - SVM模型估计小麦样品中重金属含量的方法是可行的。
