Forecasting toxic metal concentrations in an inland sea ecosystem with machine learning algorithms

In recent years, statistical and data-driven modeling approaches have been increasingly employed to predict element concentrations and to examine relationships among environmental features. In this context, the integration of feature selection techniques with machine learning models enhances model generalization and reduces model complexity by enabling the identification of key elements that are strongly associated with the target feature. This study applies machine learning models to investigate the relationships between Aluminum (Al) and other elements and to predict Al concentration levels in an inland marine ecosystem. Specifically, the study evaluates whether accurate predictions can be achieved using a reduced subset of informative elements rather than the full feature set. The findings demonstrate that machine learning methods, when combined with feature selection, can successfully predict Al concentrations while yielding more interpretable models based on a limited number of significant elements.