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Machine Learning Algorithms for Modeling Carbon Flux in Ecosystems
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Author(s): Ruchi Rai (Shri Ram Group of Colleges, Muzaffarnagar, India), Abhishek Rai (S.D. College of Engineering, Muzaffarnagar, India), Ankur Rohilla (Shri Ram Group of Colleges, Muzaffarnagar, India)and Nishu Bhardwaj (Shri Ram Group of Colleges, Muzaffarnagar, India)
Copyright: 2025
Pages: 36
Source title:
Advanced Systems for Monitoring Carbon Sequestration
Source Author(s)/Editor(s): Hari Mohan Pandey (Bournemouth University, UK), Pawan Kumar Goel (Raj Kumar Goel Institute of Technology, India), Vipin Balyan (Cape Peninsula University of Technology, South Africa)and Satya Prakash Yadav (Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India)
DOI: 10.4018/979-8-3373-2091-5.ch014
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Abstract
Accurately modeling carbon flux in ecosystems is essential for understanding the global carbon cycle and developing effective strategies to combat climate change. Machine learning (ML) algorithms have emerged as powerful tools to address the complexity of carbon flux dynamics. This book chapter explores the application of ML techniques, including regression models, decision trees, random forests, and deep learning, to model critical components of carbon flux, such as gross primary production, ecosystem respiration, and net ecosystem exchange. These algorithms excel in capturing nonlinear relationships and interactions among environmental variables, offering insights into the effects of land-use changes, deforestation, and climate variability. The chapter also addresses key challenges, including data integration, uncertainty quantification, and computational demands, while identifying opportunities for future research. The transformative potential of ML in advancing carbon flux modeling underscores its importance for ecosystem monitoring and sustainable environmental management.
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