Predicting the pulse of the Amazon: Machine learning insights into deforestation dynamics

Area of Science:

• Environmental Science
• Machine Learning Applications
• Amazonian Ecology

Background:

• Deforestation in the Brazilian Amazon poses a significant environmental challenge.
• Understanding the drivers of deforestation is crucial for effective conservation strategies.
• Previous studies have explored various factors influencing forest loss, but advanced analytical techniques can provide deeper insights.

Purpose of the Study:

• To analyze deforestation patterns in the Brazilian Amazon between 1999 and 2020.
• To identify critical factors influencing deforestation using machine learning.
• To evaluate the predictive accuracy of different machine learning models for deforestation.

Main Methods:

• Utilized machine learning techniques, specifically Random Forest, for deforestation analysis.
• Assessed 16 critical factors potentially related to deforestation.
• Evaluated model performance using determination coefficient, mean squared error, and mean absolute error.

Main Results:

• The harvested area of permanent crops was identified as the most influential variable predicting deforestation.
• The area of temporary crops was the second most significant factor.
• A significant inverse relationship was found between public spending and deforestation rates.

Conclusions:

• Machine learning, particularly Random Forest, is effective for analyzing deforestation drivers.
• Agricultural expansion, specifically permanent and temporary crops, is a primary driver of Amazon deforestation.
• Increased public spending may serve as a viable strategy to mitigate deforestation in the region.