Data Prediction Based on Support Vector Machine (SVM)—Taking Soil Quality Improvement Test Soil Organic Matter as an Example

2019 5th International Conference on Energy Materials and Environment Engineering

能源学;生态环境科学

Niu, Yan^1^2^3 ; Ye, Shenglan^1^2^3

Institute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Co., Ltd., Xi'an

710075, China^1

Key Laboratory of Degraded and Unused Land Consolidation Engineering, Ministry of Land and Resources, Xi'an

710075, China^2

Shaanxi Provincial Land Consolidation Engineering Technology Research Center, Xi'an

710075, China^3

关键词: Data classification;    Data prediction;    Operationality;    Relative errors;    Soil organic matters;    Soil quality;    Statistical learning theory;    Support vector;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/295/2/012021/pdf
DOI  :  10.1088/1755-1315/295/2/012021

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Support Vector Machine (SVM) is a machine learning language based on statistical learning theory, mainly used for data classification and regression analysis. Taking the soil quality improvement test soil sample organic matter data as an example, the support vector machine is used to train and predict the data, and the relative error between the predicted value and the actual sample value is analyzed to verify the support vector machine data prediction in the field of land engineering. Operationality, pointing out the inadequacies, in order to provide reference for relevant data analysis.