Application of machine learning techniques in the predictability of the revenue of the Financial Compensation for Mineral Exploitation (CFEM): A case study in the Municipality of Parauapebas, State of Pará (PA), Brazil
DOI:
https://doi.org/10.33448/rsd-v14i6.48913Keywords:
CFEM; Revenue forecasting; Mining; Parauapebas; Machine learning.Abstract
This article aims to apply supervised machine learning models, specifically the Random Forest Regressor and K-Nearest Neighbors (KNN) algorithms, to predict the revenue collected by the Financial Compensation for Mineral Exploitation (CFEM) in the Municipality of Parauapebas, State of Pará (PA), Brazil. This article presents an application of machine learning models to forecast CFEM revenue, focusing on the municipality of Parauapebas. The research is characterized as applied, quantitative, and descriptive, using supervised regression algorithms — Random Forest and K-Nearest Neighbors — applied to public data from the National Mining Agency (ANM), from 2015 to 2024. The results showed that the Random Forest model had the best predictive performance compared to KNN, achieving a coefficient of determination (R²) above 0.90. The forecast for January 2025 indicated a revenue exceeding BRL 100 million, highlighting the potential of predictive modeling to support municipal budget planning. It is concluded that artificial intelligence techniques can significantly contribute to public management in mining municipalities, especially in scenarios with high dependence on CFEM revenues.
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