Predicción de la Deforestación en Caldas, Colombia: Un Enfoque Sociodemográfico mediante el Uso de Modelos Machine Learning

Abstract

In the context of growing concerns about deforestation and its environmental and social impacts, this research focuses on understanding the relationships between sociodemographic characteristics and deforestation in the department of Caldas, Colombia. Purpose. The main purpose of this study is to develop a regression-based Machine Learning model that predicts deforestation based on sociodemographic variables, aiming to facilitate territorial planning and deforestation mitigation in the region. Methodology. The research was conducted by collecting sociodemographic data from the National Department of Statistics (DANE) and satellite records of deforestation from the University of Maryland. These datasets were integrated to analyze the relationship between population and deforestation. Machine Learning algorithms and computational tools were used to develop and evaluate regression models. Results. The results reveal significant relationships between sociodemographic variables and deforestation in Caldas. The Machine Learning model accurately predicted deforestation, providing a valuable tool for data-driven territorial planning. Conclusions. According to these results, the need to implement territorial planning policies that take into account population characteristics becomes evident. These characteristics include age, especially those above 60 years old, educational levels, considering that individuals with higher education tend to contribute less to deforestation, and the occupation of the population. These elements are highlighted as key factors in the management and mitigation of deforestation in Caldas.