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Forecasting toxic metal concentrations in an inland sea ecosystem with machine learning algorithms
Aylin Ucan1, Nihat Tak2, Asli Hocaoglu-Ozyigit3
1Department of Statistics, Marmara University, 34730, Istanbul, Turkey.
Scientific Reports
|April 13, 2026
Summary
Machine learning models with feature selection accurately predict Aluminum (Al) concentrations in marine ecosystems. This approach identifies key elements, simplifying models for better interpretation and prediction of Al levels.
Area of Science:
- Environmental Science
- Data Science
- Geochemistry
Background:
- Statistical and data-driven modeling are crucial for environmental feature analysis.
- Feature selection integrated with machine learning improves model generalization and reduces complexity.
- Understanding element relationships is key to predicting environmental concentrations.
Purpose of the Study:
- To investigate relationships between Aluminum (Al) and other elements.
- To predict Al concentration levels in an inland marine ecosystem using machine learning.
- To evaluate the efficacy of using a reduced feature set versus a full set for accurate Al prediction.
Main Methods:
- Application of machine learning models for predictive analysis.
- Integration of feature selection techniques to identify significant elements.
- Comparative analysis of model performance using full and reduced feature sets.
Main Results:
- Machine learning models successfully predicted Al concentrations.
- Feature selection identified a subset of informative elements crucial for prediction.
- Models utilizing reduced feature sets yielded accurate and more interpretable results.
Conclusions:
- Machine learning combined with feature selection is effective for predicting Al concentrations.
- A reduced set of significant elements enhances model interpretability and predictive power.
- This approach offers a robust method for environmental element analysis in marine ecosystems.
