Demystifying Machine Learning: Applications in African Environmental Science and Engineering

Abstract

This article delves into the transformative role of Machine Learning (ML) in Environmental Science and Engineering (ESE), illustrating its broad applications across diverse environmental issues and its potential to enhance decision-making and operational efficiency. It emphasizes the integration of ML techniques such as regression analysis, anomaly detection, and deep learning to address complex challenges in climate change, energy management, water resource management, and more. The document particularly focuses on the adaptation and challenges of ML in the African context, highlighting barriers such as infrastructure limitations and data scarcity, while proposing innovative solutions like cloud computing and lightweight models. Practical use cases in meteorology, energy optimization, and water management underscore the practical impacts of ML, showing significant advancements in forecasting, resource management, and system optimization. The article also discusses methodological considerations necessary for effective ML application in ESE, including model selection and rigorous benchmarking. Ultimately, it provides a comprehensive overview of current capabilities, challenges, and future directions for ML in environmental science, advocating for continued innovation and tailored solutions to meet the unique needs of different regions, particularly Africa.