Estimación de índices de estabilidad de agregados de suelo mediante redes neuronales artificiales y modelos de regresión múltiple linear

Maryam Marashi, Ali Mohammadi Torkashvand, Abbas Ahmadi, Mehrdad Esfandyari


Durante las últimas décadas se ha utilizado un sistema de inteligencia artificial para desarrollar funciones de pedotransferencia (PTFs) que permiten estimar las propiedades del suelo. En este trabajo se evaluó la capacidad del modelo de regresión múltiple linear (MLR) y de las redes neuronales artificiales (ANNs) para desarrollar PTFs que permitan estimar el diámetro medio ponderado (MWD) a partir de propiedades rutinarias del suelo (P1) y de la combinación de propiedades rutinarias del suelo y agregados de dimension fractal (P2). Los resultados mostraron que el modelo ANN para estimar el MWD es más exacto que el modelo MLR. La aplicación de la dimensión fractal de los agregados como herramienta de predicción en ambos métodos mejoró la exactitud de las PTFs.

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