Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header

Edwin Pino-Vargas (), Edgar Taya-Acosta (), Eusebio Ingol-Blanco and Alfonso Torres-Rúa
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Edwin Pino-Vargas: Department of Civil Engineering, Jorge Basadre Grohmann National University, Tacna 23000, Peru
Edgar Taya-Acosta: Department of Computer Engineering and Systems, Jorge Basadre Grohmann National University, Tacna 23000, Peru
Eusebio Ingol-Blanco: Department of Water Resources, National Agrarian University La Molina, Lima 15012, Peru
Alfonso Torres-Rúa: Utah Water Research Laboratory, Civil and Environmental Department, Utah State University, Logan, UT 84322, USA

Agriculture, 2022, vol. 12, issue 12, 1-15

Abstract: Accurately estimating and forecasting evapotranspiration is one of the most important tasks to strengthen water resource management, especially in desert areas such as La Yarada, Tacna, Peru, a region located at the head of the Atacama Desert. In this study, we used temperature, humidity, wind speed, air pressure, and solar radiation from a local weather station to forecast potential evapotranspiration (ETo) using machine learning. The Feedforward Neural Network (Multi-Layered Perceptron) algorithm for prediction was used under two approaches: “direct” and “indirect”. In the first one, the ETo is predicted based on historical records, and the second one predicts the climate variables upon which the ETo calculation depends, for which the Penman-Monteith, Hargreaves-Samani, Ritchie, and Turc equations were used. The results were evaluated using statistical criteria to calculate errors, showing remarkable precision, predicting up to 300 days of ETo. Comparing the performance of the approaches and the machine learning used, the results obtained indicate that, despite the similar performance of the two proposed approaches, the indirect approach provides better ETo forecasting capabilities for longer time intervals than the direct approach, whose values of the corresponding metrics are MAE = 0.033, MSE = 0.002, RMSE = 0.043 and RAE = 0.016.

Keywords: evapotranspiration; forecasting; machine learning; deep learning; arid zones (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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