Prediction model and demonstration of regional agricultural carbon emissions based on PCA-GS-KNN: a case study of Zhejiang province, China
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 compon...
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| Veröffentlicht in: | Environmental Research Communications 2023-05, Vol.5 (5), p.51001 |
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| container_title | Environmental Research Communications |
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| creator | Qi, Yanwei Liu, Huailiang Zhao, Jianbo Xia, Xinghua |
| description | 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. |
| doi_str_mv | 10.1088/2515-7620/acd0f7 |
| format | Article |
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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.</description><identifier>ISSN: 2515-7620</identifier><identifier>EISSN: 2515-7620</identifier><identifier>DOI: 10.1088/2515-7620/acd0f7</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Algorithms ; Carbon ; Emissions ; grid search ; K-nearest neighbors regression ; model prediction ; Parameters ; Prediction models ; principal component analysis ; Principal components analysis ; regional agricultural carbon emissions ; Regression models</subject><ispartof>Environmental Research Communications, 2023-05, Vol.5 (5), p.51001</ispartof><rights>2023 The Author(s). Published by IOP Publishing Ltd</rights><rights>2023 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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Res. Commun</addtitle><description>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. 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| subjects | Algorithms Carbon Emissions grid search K-nearest neighbors regression model prediction Parameters Prediction models principal component analysis Principal components analysis regional agricultural carbon emissions Regression models |
| title | Prediction model and demonstration of regional agricultural carbon emissions based on PCA-GS-KNN: a case study of Zhejiang province, China |
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