GS-SVR: Analysis and Prediction of Henan Province Grain Production Using Support Vector Regression

被引：1

作者：

Wu, Jiadi ^{[1
,2
]}

Wei, Yuanyuan ^{[3
]}

Huang, He ^{[1
]}

机构：

[1] Chinese Acad Sci, Intelligent Agr Engn Lab Anhui Prov, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China

[2] Univ Sci & Technol China, Hefei 230026, Peoples R China

[3] Anhui Univ, Sch Internet, Hefei 230039, Peoples R China

来源：

PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年

关键词：

Grain Production; Support Vector Regression; GS-SVR; MACHINES;

D O I：

10.1109/CCDC52312.2021.9602825

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Chinese government always attaches great importance to food security. The grain output of Henan Province is of great significance to guarantee China's food security. We select the Henan Province Food Production Data Set from 1990 to 2018 in the Henan Statistical Yearbook, including total grain output, sown area, irrigation area, etc. We propose a GS-SVR model to analyze and predict grain production in Henan Province. At the same time, the traditional support vector regression (SVR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) three methods are compared in this experiment, the experiment shows: The accuracy of the GS-SVR model for predicting grain output in Henan Province exceeds 96%. The GS-SVR model performs better than the other three models.

引用

页码：2264 / 2268

页数：5

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