Machine learning for land use change analysis in environmental protection areas.

Anthropogenic land use and land cover change (LULCC), combined with ongoing climate variability, poses significant challenges to environmental protection areas (EPAs) by altering ecosystem structure, degrading vegetation integrity, and disrupting local climate regulation. Despite their importance, traditional LULCC approaches often fail to incorporate dynamic environmental drivers, limiting their capacity to represent complex landscape-climate interactions. This study investigates the environmental dynamics of the Guaraqueçaba Environmental Protection Area and evaluates the landscape's potential for automated classification and prediction of impacts associated with land use change. A multitemporal dataset spanning 15 years (2009-2023) was analyzed, comprising an original set of approximately 30.2 million records of annual time series of precipitation, maximum and minimum temperatures, evapotranspiration, global solar radiation, relative humidity, wind speed, land use and land cover information, and the Normalized Difference Vegetation Index (NDVI). To address class imbalance, a balanced subset of 3.6 million records was used for modeling. Predictive models were developed using multiple linear regression (MLR), k-nearest neighbors (KNN), and random forest (RF), with performance assessed under both imbalanced and balanced data conditions using accuracy,