Estimación de evapotranspiración de referencia con información escasa utilizando machine learning en el suroccidente colombiano

Autores/as

  • Juan Camilo Triana-Madrid Universidad del Valle
  • Camilo Ocampo-Marulanda Universidad del Valle, Fundación Universitaria de San Gil
  • Yesid Carvajal-Escobar Universidad del Valle
  • Wilmar Alexander Torres-López Universidad del Valle
  • Joshua Triana Universidad del Valle
  • Teresita Canchala Universidad del Valle

DOI:

https://doi.org/10.24215/1850468Xe024

Palabras clave:

redes neuronales artificiales, FAO-PM56 Penman-Monteith, métricas de desempeño, Suroccidente Colombiano, evapotranspiración

Resumen

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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Biografía del autor/a

Juan Camilo Triana-Madrid, Universidad del Valle

Water Resources Engineering and Soil Research Group (IREHISA), School of Natural Resources and Environmental Engineering, Universidad del Valle, Cali 25360, Colombia

Camilo Ocampo-Marulanda, Universidad del Valle, Fundación Universitaria de San Gil

Water Resources Engineering and Soil Research Group (IREHISA), School of Natural Resources and Environmental Engineering, Universidad del Valle, Cali 25360, Colombia
Faculty of Natural Sciences and Engineering, Fundación Universitaria de San Gil, Yopal 850007, Colombia

Yesid Carvajal-Escobar, Universidad del Valle

Water Resources Engineering and Soil Research Group (IREHISA), School of Natural Resources and Environmental Engineering, Universidad del Valle, Cali 25360, Colombia

Wilmar Alexander Torres-López, Universidad del Valle

Research Group in Applied Statistics (INFERIR), School of Statistics, Universidad del Valle, Cali 25360, Colombia

Joshua Triana, Universidad del Valle

Univalle Group in Artificial Intelligence (GUIA), School of Systems and Computer Engineering, Universidad del Valle, Cali 25360, Colombia

Teresita Canchala, Universidad del Valle

Water Resources Engineering and Soil Research Group (IREHISA), School of Natural Resources and Environmental Engineering, Universidad del Valle, Cali 25360, Colombia

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04-10-2023

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