Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management.

Este artículo plantea el desarrollo de un Sistema Tutorial Inteligente (STI), para acompañar el proceso de aprendizaje del Álgebra por medio de una técnica de Inteligencia Artificial (IA). Para el desarrollo del sistema, se usó la siguiente metodología: se realizó una caracterización de los tópicos del Álgebra; luego, se diseñaron los módulos de «Dominio, Interfaz», «Gamificación» y «Estudiante» del Sistema Tutorial Inteligente, para la construcción de un prototipo de STI; posteriormente, se seleccionó una técnica de IA para el diseño del módulo tutor.La novedad de este prototipo consiste en su capacidad de adaptación al conocimiento del estudiante, por medio de una Red Neuronal Artificial, permitiéndole responder ante las necesidades de aprendizaje que se presenten, haciendo uso del conocimiento del estudiante respecto a los distintos tópicos del Álgebra, informacipon que se obtiene a través de un historial generado a través del uso del sistema, lo que favorece la selección de la actividad más pertinente para cada estudiante.