Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/12/1971 |
| _version_ | 1827642354441388032 |
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| author | Edwin Pino-Vargas Edgar Taya-Acosta Eusebio Ingol-Blanco Alfonso Torres-Rúa |
| author_facet | Edwin Pino-Vargas Edgar Taya-Acosta Eusebio Ingol-Blanco Alfonso Torres-Rúa |
| author_sort | Edwin Pino-Vargas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T17:27:15Z |
| format | Article |
| id | doaj.art-b8f9874eef934c36a6aa3e0cc074e6d3 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T17:27:15Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-b8f9874eef934c36a6aa3e0cc074e6d32023-11-24T12:39:06ZengMDPI AGAgriculture2077-04722022-11-011212197110.3390/agriculture12121971Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert HeaderEdwin Pino-Vargas0Edgar Taya-Acosta1Eusebio Ingol-Blanco2Alfonso Torres-Rúa3Department of Civil Engineering, Jorge Basadre Grohmann National University, Tacna 23000, PeruDepartment of Computer Engineering and Systems, Jorge Basadre Grohmann National University, Tacna 23000, PeruDepartment of Water Resources, National Agrarian University La Molina, Lima 15012, PeruUtah Water Research Laboratory, Civil and Environmental Department, Utah State University, Logan, UT 84322, USAAccurately 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.https://www.mdpi.com/2077-0472/12/12/1971evapotranspirationforecastingmachine learningdeep learningarid zones |
| spellingShingle | Edwin Pino-Vargas Edgar Taya-Acosta Eusebio Ingol-Blanco Alfonso Torres-Rúa Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header Agriculture evapotranspiration forecasting machine learning deep learning arid zones |
| title | Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header |
| title_full | Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header |
| title_fullStr | Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header |
| title_full_unstemmed | Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header |
| title_short | Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header |
| title_sort | deep machine learning for forecasting daily potential evapotranspiration in arid regions case atacama desert header |
| topic | evapotranspiration forecasting machine learning deep learning arid zones |
| url | https://www.mdpi.com/2077-0472/12/12/1971 |
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