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AI and Machine Learning in Carbon Sequestration
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Author(s): Anurag Upadhyay (KCCITM Greater Noida, India), Anshul Kumar (IIMT College of Management, Greater Noida, India), Ajeet Kumar Sharma (Department of CSE, Sharda School of Computing Science & Engineering (SSCSE), Sharda University, Greater Noida, India), Avinash Kumar Kumar Sharma (Department of CSE, Sharda School of Computing Science & Engineering (SSCSE), Sharda University, Greater Noida, India), Saurabh Singhal (Greater Noida Institute of Technology, India)and Pawan Kumar Goel (Raj Kumar Goel Institute of Technology, Ghaziabad, India)
Copyright: 2025
Pages: 20
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.ch005
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Abstract
Carbon sequestration, which collects and stores carbon dioxide from the atmosphere, is a crucial tactic in the fight against climate change. The precision, scalability, and efficiency of carbon sequestration techniques could be revolutionized by advanced computing techniques like artificial intelligence (AI) and machine learning (ML). We examine how machine learning and artificial intelligence can transform carbon sequestration, with a particular emphasis on how they can be applied to enhance carbon restriction methods, maximize land use plans, and track carbon flow rates.
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