Authors : Anya Adebayo, ANYA; Kelechi Adura, ANYA; Eke Kehinde ANYA; Akinwale Victor, ISHOLA

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/4rzts23t

Scribd : https://tinyurl.com/4y9hrxm2

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV028

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Climate change presents profound challenges for the African continent, necessitating effective adaptation and mitigation strategies to enhance resilience. This paper explores the role of predictive analytics in developing climate-resilient approaches for Africa, emphasizing its significance in understanding, quantifying, and addressing climate-related risks. The study examined the impact of predictive insights across various sectors, including agriculture, climate finance, and supply chain management, highlighting how data- driven decision-making can inform policy frameworks and drive sustainable investment. Furthermore, it analysed existing adaptation strategies, such as the use of climate-resilient crop varieties and early warning systems, underscoring the importance of integrating these approaches into national policies. Despite the potential of predictive analytics, the paper also addresses inherent challenges, including data quality issues and model uncertainty, which can hinder effective implementation. The study offers recommendations for fostering a collaborative and integrated approach to building a climate-resilient Africa through robust data- driven mitigation and adaptation strategies, advocating for enhanced policy support, funding, and cross-sector collaboration.

Keywords : Climate, Climate Change, Climate Resilience, Machine Learning (ML).

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