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Assessment of Advanced Machine and Deep Learning Approaches for Predicting CO2 Emissions from Agricultural Lands: Insights Across Diverse Agroclimatic Zones

Crossref DOI link: https://doi.org/10.1007/s41748-024-00424-x

Published Online: 2024-07-03

Published Print: 2024-12

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

Authors

Harsányi, Endre

Mirzaei, Morad

Arshad, Sana http://orcid.org/0000-0003-4809-5742
Alsilibe, Firas

Vad, Atilla

Nagy, Adrian

Ratonyi, Tamás

Gorji, Manouchehr

Al-Dalahme, Main

Mohammed, Safwan http://orcid.org/0000-0003-2311-6789
Funding

Funding for this research was provided by:

University of Debrecen
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Text and Data Mining valid from 2024-07-03

Version of Record valid from 2024-07-03
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Article History

Received: 22 February 2024

Revised: 21 May 2024

Accepted: 21 June 2024

First Online: 3 July 2024

Declarations

:

: The author(s) declare no competing interests.

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