APPLICATION OF MACHINE LEARNING AND DEEP LEARNING METHODS IN WATER QUALITY MODELING AND PREDICTION: PROSPECTS FOR ACHIEVING SDG 6 IN SUB-SAHARAN AFRICA

Abstract

Water quality modelling and prediction are critical for achieving Sustainable Development Goal 6 (SDG 6), which seeks to ensure the availability and sustainable management of water and sanitation for all. In Sub-Saharan Africa, where access to clean water remains a pressing challenge, innovative approaches such as machine learning (ML) and deep learning (DL) offer promising solutions for enhancing water quality monitoring and management. This paper explores the application of ML and DL techniques in water quality modelling and prediction, focusing on their potential to address regional water challenges. Reviewing existing studies, we identify key algorithms and models to predict various water quality parameters, such as pH, turbidity, and pollutant concentrations. We also highlight the benefits of these data-driven approaches, including their ability to handle complex, non-linear relationships and large datasets, enabling more accurate and real-time predictions. Additionally, we discuss the challenges in implementing these technologies, including data availability, technical infrastructure, and capacity-building needs in Sub-Saharan Africa. The paper concludes by outlining prospects for scaling these technologies to improve water management systems in line with the achievement of SDG 6. ML and DL have the potential to significantly contribute to sustainable water resource management in the region, offering a pathway to enhance water quality, reduce health risks, and ensure a safe water supply for all.

Keywords: Machine learning, Deep learning, Water quality modelling, Water quality prediction, Sustainable Development Goal 6 (SDG 6), Sub-Saharan Africa, Water management, Data-driven approaches, Environmental monitoring, Clean water