Prediction model and demonstration of regional agricultural carbon emissions based on PCA-GS-KNN: a case study of Zhejiang province, China
Yanwei Qi, Huailiang Liu, Jianbo Zhao, Xinghua Xia
Abstract
Abstract The paper proposes a prediction algorithm that is composed with principal component analysis (PCA), grid search (GS) and K-nearest neighbours (KNN). Firstly, in order to solve the problem of multicollinearity in multiple regression, principal component analysis is used to select the principal components of the regression variables; then, the K-nearest neighbour regression prediction model is used to train the data and the grid search is used to obtain better prediction model parameters in order to solve the problem of difficult parameter selection in the traditional K-nearest neighbour regression prediction model; finally, taking Zhejiang Province, China, as an example, the optimised prediction model is used to conduct regional agricultural carbon emission. The results show that the algorithm outperforms other prediction models in terms of prediction accuracy and it can accurately predict regional agricultural carbon emissions.