Predicción de la evapotranspiración en la región pampeana por medio de datos CERES y técnicas de aprendizaje automático
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https://doi.org/10.24215/1850468Xe021Palabras clave:
evapotranspiraci´on, aprendizaje autom´atico, CERES, teledetecci´onResumen
Un aspecto clave en zonas agrícolas, como la llanura Pampeana argentina, es poder estimar con precisión las tasas de evapotranspiración para optimizar cultivos y requerimientos de riego, como así también la predicción de inundaciones y sequías. En este sentido, se evaluaron seis algoritmos de aprendizaje automático para estimar la evapotranspiraci´on de referencia y la evapotranspiración real (ET0 y ETa, respectivamente) utilizando productos de satélite CERES como datos de entrada. Los valores modelados, aplicando técnicas de aprendizaje automático, se compararon con aquellos obtenidos a partir de información de terreno. Después de entrenar y validar los algoritmos, observamos que el Regresor con Vectores de Soporte (SVR) mostraba la mejor precisión. A continuación, con un conjunto de datos independiente, se testearon los algoritmos SVR calibrados. Para la predicción de la evapotranspiración de referencia se observaron errores estadÍsticos de MAE =0.437 mm d−1 y RMSE = 0.616 mm d−1, con un coeficiente de determinación R2= 0.893. Por otro lado, al predecir la evapotranspiración real, observamos errores estadísticos de MAE y RMSE de 0.422 mm d−1 y 0.599 mm d−1, respectivamente, con un R2 de 0.614. Al comparar los resultados obtenidos con los algoritmos de aprendizaje automático con aquellos arrojados por estudios en la misma área, entendemos que los resultados aquí mostrados son prometedores y representan una l´ınea de base para futuros trabajos. La combinación de datos de CERES con información de otras fuentes puede generar productos de evapotranspiración más específicos, considerando además las diferentes coberturas del suelo.
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Derechos de autor 2023 Facundo Carmona, Ad´an Farami˜n´an, Ra´ul Rivas, Facundo Orte
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