A Machine Learning-based Analysis of Climate Change

Year : 2024 | Volume :13 | Issue : 02 | Page : 1-10
By

Jibin Jacob Mani,

A.C. Subhajini,

  1. Research Scholar, Noorul Islam Centre for Higher Education (NICHE), Kumaramcoil, Thuckalay Kanyakumari, Tamilnadu, India
  2. Associate Professor, Noorul Islam Centre for Higher Education (NICHE), Kumaramcoil, Thuckalay Kanyakumari, Tamilnadu, India

Abstract

Climatic 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.

Keywords: Clustering, machine learning, greenhouse gas, finite-time thermodynamics, climate change.

[This article belongs to Research & Reviews : Journal of Space Science & Technology(rrjosst)]

How to cite this article: Jibin Jacob Mani, A.C. Subhajini. A Machine Learning-based Analysis of Climate Change. Research & Reviews : Journal of Space Science & Technology. 2024; 13(02):1-10.
How to cite this URL: Jibin Jacob Mani, A.C. Subhajini. A Machine Learning-based Analysis of Climate Change. Research & Reviews : Journal of Space Science & Technology. 2024; 13(02):1-10. Available from: https://journals.stmjournals.com/rrjosst/article=2024/view=167369

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Regular Issue Subscription Review Article
Volume 13
Issue 02
Received July 5, 2024
Accepted July 27, 2024
Published August 16, 2024
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