Adoption of Machine Learning Technologies in Mitigation of Climate Change Risks in North Rift, Kenya

Abstract

This study examines the implementation and effectiveness of Machine Learning (ML) technologies in addressing climate change risks within Kenya's North Rift region. The research investigates how ML applications are being utilized to enhance climate resilience, improve agricultural practices, and support decision-making processes in climate risk management. Through a mixed-methods approach combining quantitative data analysis and qualitative stakeholder interviews, this study evaluates the current state of ML adoption, identifies key challenges, and assesses the impact on local communities. Findings indicate that while ML adoption is still in its early stages, there is significant potential for these technologies to improve climate risk prediction, optimize resource allocation, and enhance adaptation strategies. The study reveals that successful implementation requires addressing infrastructure limitations, building local capacity, and ensuring community engagement. This research contributes to the growing body of knowledge on technological solutions for climate change adaptation in developing regions and provides practical recommendations for policymakers and practitioners.