Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions
A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature...
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MDPI AG
2024-02-01
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| Online Access: | https://www.mdpi.com/2073-4441/16/4/602 |
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| author | Ahmed Skhiri Ali Ferhi Anis Bousselmi Slaheddine Khlifi Mohamed A. Mattar |
| author_facet | Ahmed Skhiri Ali Ferhi Anis Bousselmi Slaheddine Khlifi Mohamed A. Mattar |
| author_sort | Ahmed Skhiri |
| collection | DOAJ |
| description | A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R<sup>2</sup>), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day<sup>−1</sup>) and RMSE (0.326 mm day<sup>−1</sup>) were also obtained. |
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| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-07T22:09:52Z |
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| spelling | doaj.art-250b4f1b75b145fe8ab044b85f8e5fbf2024-02-23T15:38:04ZengMDPI AGWater2073-44412024-02-0116460210.3390/w16040602Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic RegionsAhmed Skhiri0Ali Ferhi1Anis Bousselmi2Slaheddine Khlifi3Mohamed A. Mattar4Research Unit Sustainable Management of Soil and Water Resources (GDRES), Higher School of Engineers of Medjez El Bab, University of Jendouba, Medjez El Bab 9070, TunisiaResearch Unit Sustainable Management of Soil and Water Resources (GDRES), Higher School of Engineers of Medjez El Bab, University of Jendouba, Medjez El Bab 9070, TunisiaDirection of Technology Transfer and Studies, National Institute of Field Crops, Bou Salem 8170, TunisiaResearch Unit Sustainable Management of Soil and Water Resources (GDRES), Higher School of Engineers of Medjez El Bab, University of Jendouba, Medjez El Bab 9070, TunisiaPrince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi ArabiaA correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R<sup>2</sup>), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day<sup>−1</sup>) and RMSE (0.326 mm day<sup>−1</sup>) were also obtained.https://www.mdpi.com/2073-4441/16/4/602reference evapotranspirationartificial neural networkBlaney–CriddleRiouTurc |
| spellingShingle | Ahmed Skhiri Ali Ferhi Anis Bousselmi Slaheddine Khlifi Mohamed A. Mattar Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions Water reference evapotranspiration artificial neural network Blaney–Criddle Riou Turc |
| title | Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions |
| title_full | Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions |
| title_fullStr | Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions |
| title_full_unstemmed | Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions |
| title_short | Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions |
| title_sort | artificial neural network for forecasting reference evapotranspiration in semi arid bioclimatic regions |
| topic | reference evapotranspiration artificial neural network Blaney–Criddle Riou Turc |
| url | https://www.mdpi.com/2073-4441/16/4/602 |
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