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Articulo S.S.A.A Ingles Final by LudovicuX Saravia Rivera is a document available to read on EtoBox.

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This study assesses the effectiveness of three machine learning models—Random Forest, Support Vector Regression, and Extreme Gradient Boosting—in predicting pollution parameters at the Santa Catalina tailings pond in Peru. The results indicate that XGBoost outperformed the other models with high accuracy metrics, identifying areas of environmental impact related to acid mine drainage. The findings highlight the potential of machine learning as a tool for sustainable management of mining-related environmenta

Author

LudovicuX Saravia Rivera

Language

EN