1 Cambridge Engineering Consultants, Amman. Jordan.
2 Independent Researcher, UK.
3 Independent Researcher, Abuja, Nigeria.
4 Independent Researcher, Uyo, Nigeria.
5 Department of Mechanical Engineering, The University of Alabama, United States of America.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(01), 1999-2008
Received on 11 December 2023; revised on 20 January 2024; accepted on 22 January 2024
The burgeoning threat of climate change has spurred an increased reliance on advanced technologies to comprehend and mitigate its far-reaching consequences. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as indispensable tools in climate change research, offering unprecedented capabilities for predictive modeling and assessing environmental impact. This review synthesizes the current state of AI and ML applications in climate change research, emphasizing their role in predictive modeling and understanding environmental repercussions. Predictive models leveraging AI and ML algorithms have demonstrated remarkable efficacy in forecasting climate patterns, extreme weather events, and sea-level rise. These models incorporate vast datasets encompassing meteorological, geospatial, and oceanic information, enabling more accurate predictions of future climate scenarios. Moreover, AI-driven models excel in recognizing intricate patterns and non-linear relationships within climate data, enhancing their capacity to simulate complex environmental systems. Environmental impact assessment stands as a critical facet of climate change research, and AI and ML techniques are proving instrumental in this regard. These technologies facilitate the analysis of diverse ecological parameters, including deforestation rates, biodiversity loss, and carbon sequestration dynamics. By discerning nuanced patterns within immense datasets, AI systems contribute to a more nuanced understanding of the direct and indirect consequences of climate change on ecosystems. Despite these advancements, challenges persist, such as the need for standardized data formats, model interpretability, and ethical considerations. Additionally, the integration of AI and ML findings into policy frameworks remains a crucial frontier. As the intersection of AI, ML, and climate change research evolves, continuous interdisciplinary collaboration is essential to harness the full potential of these technologies in safeguarding our planet's future. This review illuminates the current landscape of AI and ML applications, providing insights into their efficacy, challenges, and potential contributions to advancing climate change research and environmental sustainability.
AI; Machine learning; Climate Change; Predictive Models; Environmental Impact; Review
Preview Article PDF
Ahmad Hamdan, Kenneth Ifeanyi Ibekwe, Emmanuel Augustine Etukudoh, Aniekan Akpan Umoh and Valentine Ikenna Ilojianya. AI and machine learning in climate change research: A review of predictive models and environmental impact. World Journal of Advanced Research and Reviews, 2024, 21(1), 1999-2008. Article DOI: https://doi.org/10.30574/wjarr.2024.21.1.0257