  Water resources are essential for sustaining human livelihoods and
environmental well being. Accurate water quality prediction plays a pivotal
role in effective resource management and pollution mitigation. In this study,
we assess the effectiveness of five distinct predictive models linear
regression, Random Forest, XGBoost, LightGBM, and MLP neural network, in
forecasting pH values within the geographical context of Georgia, USA. Notably,
LightGBM emerges as the top performing model, achieving the highest average
precision. Our analysis underscores the supremacy of tree-based models in
addressing regression challenges, while revealing the sensitivity of MLP neural
networks to feature scaling. Intriguingly, our findings shed light on a
counterintuitive discovery: machine learning models, which do not explicitly
account for time dependencies and spatial considerations, outperform spatial
temporal models. This unexpected superiority of machine learning models
challenges conventional assumptions and highlights their potential for
practical applications in water quality prediction. Our research aims to
establish a robust predictive pipeline accessible to both data science experts
and those without domain specific knowledge. In essence, we present a novel
perspective on achieving high prediction accuracy and interpretability in data
science methodologies. Through this study, we redefine the boundaries of water
quality forecasting, emphasizing the significance of data driven approaches
over traditional spatial temporal models. Our findings offer valuable insights
into the evolving landscape of water resource management and environmental
protection.
