Estimation of Monthly Reference Evapotranspiration with Scarce Information Using Machine Learning in Southwestern Colombia

Authors

  • 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

Keywords:

artificial neural network, FAO-56 Penman-Monteith, performance metrics, Southwestern Colombia, evapotranspiration

Abstract

This research aimed to identify an alternative method to estimate reference evapotranspiration (ETo) with scarce climatological information in southwestern Colombia between 1983-2017 by evaluating and comparing different machine learning techniques. The FAO Penman-Monteith (FAO-PM56) was used as the reference method and four empirical methods (Hargreaves, Thornthwaite, Cenicafé, and Turc) were assessed with five metrics to evaluate the method of best fit to FAO-PM56, root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), Nash-Sutcliffe model efficiency coefficient (NSE), and Pearson correlation coefficient (R). Three models were designed using machine learning techniques to estimate ETo, multiple linear regression (MLR), artificial neural networks (ANN), and autoregressive integrated moving average model (ARIMA). The results showed that the ARIMA-M3 model reported the best performance metrics (RMSE = 4.13 mm month-1, MAE = 3.15 mm month-1, MBE = -0.08 mm month-1, NSE = 0.96 and r = 0.98). However, it restricts in that it can only be used locally and cannot be extrapolated to other climatological stations,because it was calibrated with specific conditions (exogenous variables) and stations,unlike the ANN-M1 model, which only requires training the network for its application. This method will allow estimating ETo in places with scarce information, as vital for water management in places with much uncertainty regarding accessibility and availability.

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Author Biographies

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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2023-10-04

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