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Jibin Jacob Mani, A.C. Subhajini,
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- Research Scholar,, Associate Professor, Noorul Islam Centre for Higher Education (NICHE), Kumaramcoil, Thuckalay Kanyakumari,, Noorul Islam Centre for Higher Education (NICHE), Kumaramcoil, Thuckalay Kanyakumari, Tamilnadu,, Tamilnadu, India, India
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Abstract
nClimatic variations are a pressing global challenge that demands immediate and comprehensive attention. A wealth of articles has been published on climate change mitigation and adaptation, yet there remains a need for innovative methods to explore the complexities of climatic variations and to devise more efficient and effective strategies for adjustment and alleviation. With technological advancements, machine learning (ML) and deep learning (DL) approaches have derived significant popularity across various fields, including climatic variations research. The objective of this paper is to investigate the most prevalent ML and DL approaches implemented to climatic variations mitigation and adaptation. Additionally, it seeks to identify the utmost common mitigation and adaptation measures, with a particular focus on urban areas, that have been investigated utilizing machine learning and deep learning methodologies. To fulfill these aims, this study utilizes word frequency analysis and topic modelling, specifically employing the Latent Dirichlet Allocation (LDA) algorithm as a machine learning tool. The findings indicate that Artificial Neural Networks (ANNs) are the predominant ML technique in both climate change mitigation and adaptation endeavours. Moreover, within various research domains concerning climate change, geoengineering and investigations into land surface temperature stand out as the fields that have most extensively employed ML and DL algorithms.
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Keywords: Clustering, machine learning, greenhouse gas, finite-time thermodynamics, climate change.
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Research & Reviews : Journal of Space Science & Technology
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Volume | 13 | |
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
Received | July 5, 2024 | |
Accepted | July 27, 2024 | |
Published | August 16, 2024 |
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