Estimación de evapotranspiración de referencia con información escasa utilizando machine learning en el suroccidente colombiano
DOI:
https://doi.org/10.24215/1850468Xe024Palabras clave:
redes neuronales artificiales, FAO-PM56 Penman-Monteith, métricas de desempeño, Suroccidente Colombiano, evapotranspiraciónResumen
Esta investigación tuvo como objetivo identificar un método alternativo para estimar la evapotranspiración de referencia (ETo) con escasa información climatológica en el suroeste de Colombia entre 1983-2017, evaluando y comparando diferentes técnicas de machine learning. Se utilizó el método de FAO Penman-Monteith (FAO-PM56) como método de referencia y se evaluaron 4 métodos de empíricos (Hargreaves, Thornthwaite, Cenicafé y Turc) con cinco métricas para evaluar el método de mejor ajuste al FAO-PM56, error cuadrático medio (RMSE), error medio absoluto (MAE), error medio de sesgo (MBE), coeficiente de eficiencia del modelo de Nash-Sutcliffe (NSE) y coeficiente de correlación de Pearson (R).Se diseñaron tres modelos utilizando técnicas de machine learning para estimar la ETo, regresión lineal múltiple (MLR), redes neuronales artificiales (ANN) y modelo de media móvil integradaautorregresiva (ARIMA).Los resultados mostraron que el modelo ARIMA-M3 presentó la mejor métrica de rendimiento (RMSE = 4,13 mmmes-1, MAE = 3,15 mmmes-1, MBE = -0,08 mmmes-1, NSE = 0,96 y R = 0,98).Sin embargo, tiene la restricción de que sólo se puede utilizar localmente y no se puede extrapolar a otras estaciones climatológicas, porque se calibró con estaciones y condiciones específicas (variables exógenas), a diferencia del modelo RNA-M1, que sólo requiere entrenar la red para su aplicación.Este método permitirá estimar la ETo en lugares con escasa información, lo que es vital para la gestión del agua en lugares con mucha incertidumbre en cuanto a accesibilidad y disponibilidad.
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Derechos de autor 2023 Juan Camilo Triana-Madrid, Camilo Ocampo-Marulanda, Yesid Carvajal-Escobar, Wilmar Alexander Torres-López, Joshua Triana, Teresita Canchala
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