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

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.