Modeling Anthropization Propensity in Morro São Pedro (Porto Alegre/RS) using Machine Learning: A Hybrid Tree-Based Approach
DOI:
https://doi.org/10.22456/1807-9806.146128Keywords:
territorial planning, ecosystems, natural resourcesAbstract
Mapping anthropization is essential for comprehending environmental changes and facilitating territorial planning. In this research, the central problem was to analyze the impacts of human activity on Morro São Pedro, Porto Alegre. The main objective was to identify how the local landscape has changed over time. To this end, three machine learning algorithms were employed: Parallel Random Forest (PRF), Regularized Random Forest (RRF), and XGBoost. The results indicated that the PRF model was the most accurate among those tested. This finding suggests that the presence of highways is a primary factor influencing changes in the natural environment. The proximity of these roads stood out as the most significant factor across all models, suggesting that the construction and use of highways play a pivotal role in the transformation of the region. In conclusion, the results of this study highlight the importance of incorporating the impacts of highways into territorial and environmental planning, particularly with regard to ecosystem conservation and natural resource management.
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