ADVANCING WATER QUALITY PREDICTION: THE ROLE OF MACHINE LEARNING IN ENVIRONMENTAL SCIENCE

Abstract

This article delves into the burgeoning domain of machine learning (ML) applications within environmental science, with a specific focus on water quality prediction. Amidst escalating environmental challenges, the precision and efficiency of ML models have emerged as pivotal tools for analyzing complex datasets, offering nuanced insights and forecasts about water quality trends. We explore the integration of ML in environmental monitoring, highlighting its comparative advantage over traditional statistical methods in handling vast, multifaceted data streams. This exploration encompasses a critical evaluation of various ML algorithms tailored for predictive accuracy in water quality assessment, including supervised and unsupervised learning models. The article also addresses the challenges inherent in ML applications, such as data quality and model interpretability, and anticipates future trajectories in this rapidly evolving field. The potential for ML to revolutionize environmental policy-making and resource management through enhanced predictive capabilities is a central theme, underscoring the transformative impact of these technologies in environmental science.