光学学报, 2020, 40 (10): 1030002, 网络出版: 2020-04-28
基于GWO-SVR的土壤镉元素含量含水率校正预测模型研究 下载: 909次
Study on Prediction Model of Soil Cadmium Content Moisture Content Correction Based on GWO-SVR
光谱学 X射线荧光光谱 含水率补偿 灰狼优化算法 支持向量回归 spectroscopy X-ray fluorescence spectrum water content compensation grey wolf optimization algorithm support vector regression
摘要
针对土壤含水率对X射线荧光光谱(XRF)法检测结果存在严重干扰的问题,提出了一种基于灰狼优化(GWO)算法的支持向量回归(SVR)校正预测模型。完成光谱数据预处理之后,基于GWO-SVR建立净峰面积、含水率与镉元素含量之间的定量分析模型,并将GWO-SVR模型与其他模型对比。结果表明:SVR非线性模型比线性回归模型有更好的决定系数、更小的误差,在GWO下,各个模型指标均得到提升;与其他优化算法相比,GWO-SVR迭代次数更少,拟合效果更好,预测误差更小。所提模型也可为土壤中其他重金属含量的预测及含水率校正提供有效的参考。
Abstract
Owing to the serious interference of soil moisture content in the detection techniques such as X-ray fluorescence spectroscopy (XRF) method, a support vector regression (SVR) correction prediction model is proposed based on the grey wolf optimization (GWO) algorithm. Subsequent to the preprocess of spectral data, a quantitative analysis model for determining the relationship among net peak area, moisture content, and cadmium content is established based on GWO-SVR. The GWO-SVR model is compared with other models. The results show that the SVR nonlinear model has a better decision coefficient and smaller errors than the linear regression model. Moreover, under the GWO optimization, each model index is improved. Compared with other optimization algorithms, GWO-SVR has less iterations, better fitting effect, and smaller prediction errors. The proposed model can provide an effective reference for the prediction of other heavy metals in soils and the correction of moisture content.
陈颖, 张灿, 肖春艳, 赵学亮, 史彦新, 杨惠, 刘峥莹, 李少华. 基于GWO-SVR的土壤镉元素含量含水率校正预测模型研究[J]. 光学学报, 2020, 40(10): 1030002. Ying Chen, Can Zhang, Chunyan Xiao, Xueliang Zhao, Yanxin Shi, Hui Yang, Zhengying Liu, Shaohua Li. Study on Prediction Model of Soil Cadmium Content Moisture Content Correction Based on GWO-SVR[J]. Acta Optica Sinica, 2020, 40(10): 1030002.