Desarrollo de modelos de aprendizaje automático para estimar temperatura del aire utilizando datos MODIS
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La temperatura del aire es una variable clave en una amplia gama de aplicaciones ambientales, que incluyen interacción tierra-atmósfera, cambio climático, modelos de cultivos e hidrológicos, entre otros. El objetivo de este estudio fue estimar las temperaturas máxima y mínima diaria, con datos de temperatura de la superficie terrestre (LST) de MODIS AQUA/TERRA, NDVI, radiación solar extraterrestre y precipitación. Se desarrollaron modelos de redes neuronales artificiales (ANN) y bosques aleatorios (RF) para predecir estas temperaturas considerando estaciones meteorológicas de Córdoba (Argentina) para el período 2018-2020. Los resultados muestran que las metodologías de RF y ANN fueron capaces de modelar relaciones no lineales entre la temperatura registrada y los datos de LST de MODIS, de manera muy robusta. La validación de los modelos confirma que Tmax y Tmin se pueden estimar con precisión utilizando, en conjunto o por separado, AQUA y TERRA LST. Los mejores modelos presentaron coeficientes de determinación iguales a 0,81/0,91 y error cuadrático medio de 2,7/2,1 ºC para Tmax/Tmin, cuando se utilizaron datos de AQUA correspondientes a día/noche, respectivamente. La solidez y el ajuste de los modelos desarrollados, sumado a la libre accesibilidad de datos a escala global, sugieren que esta metodología puede ser aplicada a otras regiones.
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