This study presents an innovative approach to support educational administration, focusing on the optimization of university admission processes using feature selection algorithms. The research addresses the challenges of concept drift, outlier treatment, and the weighting of key factors in admission criteria. The proposed methodology identifies the optimal set of features and assigns weights to the selection criteria that demonstrate the strongest correlation with academic performance, thereby contributing to improved educational management by optimizing decision-making processes. The approach incorporates concept change management and outlier detection in the preprocessing stage while employing multivariate feature selection techniques in the processing stage. Applied to the admission process of engineering students at a public Chilean university, the methodology considers socioeconomic, academic, and demographic variables, with curricular advancement as the objective. The process generated a subset of attributes and an application score with predictive capabilities of 83% and 84%, respectively. The results show a significantly greater association between the application score and academic performance when the methodology’s weights are used, compared to the actual weights. This highlights the increased predictive power by accounting for concept drift, outliers, and shared information between variables.

Climate change has a significant impact on generating forest fires. These fires damage property, interrupt productive processes, reduce employment sources, and generate direct economic losses. Also, fires contribute to climate change, resulting in a negative cycle. Therefore, the effective management of forest fires is of vital importance. This research focuses on the combat and mitigation phase of forest fires, with special emphasis on using helicopters to transport water from nearby reservoirs to the fire site. The location of these reservoirs is key since a greater distance traveled by helicopter means a longer delay in water transport, which favors the spread of the fire. For this reason, this research proposes an optimization model to determine the optimal location of these reservoirs in a territory. The proposed model is illustrated with a case study of the region of Valparaiso, demonstrating its usefulness for management and decision making when locating reservoirs for firefighting.