Modelos de distribución espacial de hidrocarburos en suelos contaminados y su implementación en software SIG. Una revisión
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Esta revisión analiza y compara los principales modelos empleados para representar la distribución espacial de hidrocarburos en suelos contaminados, con énfasis en su implementación en entornos de Sistemas de Información Geográfica (SIG). Se identificaron dos enfoques principales: métodos geoestadísticos como el Kriging ordinario y modelos de aprendizaje automático como Random Forest y redes neuronales 3D. El Kriging ordinario destaca por su equilibrio entre precisión, manejo de incertidumbre y compatibilidad con SIG, frente a la mayor complejidad de modelos avanzados. Se concluye que, el Kriging ordinario es el modelo más adecuado para representar este tipo de contaminación, y se recomienda su implementación con herramientas en Python, para lograr un análisis automatizado, preciso y accesible.
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