Mixed prediction model of agricultural CO2 emissions based on machine learning

Hao Chen

doi:10.1117/12.3029093

6 May 2024 Mixed prediction model of agricultural CO₂ emissions based on machine learning

Hao Chen

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131070D (2024) https://doi.org/10.1117/12.3029093
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

With the development of agriculture, agricultural CO₂ emissions account for an increasing share of global CO₂ emissions. However, there is still no uniform prediction standard for agricultural CO₂ emissions, so understanding and monitoring agricultural activities play a crucial role in the prediction of CO₂ emissions. In this paper, eight indicators affecting rural CO₂ emissions are selected to predict CO₂ emissions through ordinary linear regression, random forest regression, and GBDT prediction models, and experiments prove that the model in this paper still has good prediction results for complex data.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Chen "Mixed prediction model of agricultural CO₂ emissions based on machine learning", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131070D (6 May 2024); https://doi.org/10.1117/12.3029093

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KEYWORDS

Random forests

Decision trees

Machine learning

Process modeling

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