Aproximaciones neuronales univariantes para la predicción de caudales diarios en cuencas portuguesas

I. Pulido-Calvo, M. Manuela Portela

Resumen

Desde hace unos años, las redes neuronales computacionales están siendo una de las herramientas más prometedoras para la estimación de caudales en cuencas. La mayoría de los trabajos de la literatura utilizan para las predicciones, junto con los datos registrados de caudales, otras variables de entrada de carácter hidro-metorológico. En este estudio se analizó el funcionamiento de redes neuronales de retropropagación para la estimación de caudales diarios en cuencas portuguesas, considerando que sólo los datos de caudal de días previos están disponibles para la calibración de los modelos. Además de los modelos tradicionales de redes neuronales que tienen como variables de entrada los caudales en días previos, se realizó un procedimiento de convolución en las neuronas de la capa de entrada y se probó una metodología híbrida combinando redes neuronales computacionales y modelos ARIMA. Los modelos neuronales complementados con un proceso de convolución dieron las mejores estimaciones considerando los caudales de los tres días previos como variables de entrada. También se realizó un análisis preliminar de la capacidad de esta aproximación para estimar caudales diarios en una cuenca diferente a la utilizada para la calibración de los modelos, obteniéndose resultados satisfactorios.

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