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Enhancing the prediction accuracy of greenhouse temperatures through hybrid machine learning models: an in-depth comparative analysis of RBF, RF, and XGBoost techniques combined with SMA and MFO optimization strategies

Crossref DOI link: https://doi.org/10.1007/s41939-025-00886-0

Published Online: 2025-05-23

Published Print: 2024-07

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Dai, Zhenhua

Huang, Tangsen
Funding

Funding for this research was provided by:

the Key Scientific Research Foundation of Hunan Provincial Department of Education (23A0575)

the Hunan Provincial Natural Science Foundation (2024JJ7184)

the the Project of Hunan Provincial Social Science Achievement Review Committee in 2023 (XSP2023JYC283)

the Science Communication Research and Practice Project of Hunan Association for Science and Technology in 2023 (2023jckpkt096)

the General Research Projects of Hunan Provincial Department of Education (23C0358)

the Scientific Research Topics of Yongzhou Municipal Guiding Plan for Science and Technology in 2024 (2024YZ014)

the Scientific Research Projects of Hunan University of Science and Engineering in 2024 (24XKYZZ12)
License Information

Text and Data Mining valid from 2024-07-01

Version of Record valid from 2024-07-01
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Article History

Received: 9 December 2024

Accepted: 24 April 2025

First Online: 23 May 2025

Declarations

:

: The authors declare no competing interests.

: The research paper has received ethical approval from the institutional review board, ensuring the protection of participants’ rights and compliance with the relevant ethical guidelines.

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