Using artificial neural networks to predict the reference evapotranspiration
Artificial neural network models (ANNs) were used in this study to predict reference evapotranspiration ( ETo) using climatic data from the meteorological station at the test station in Kafr El-Sheikh Governorate as inputs and reference evaporation values computed using the Penman–Monteith (PM) equa...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2023-05-01
|
| Series: | Journal of Water and Land Development |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/127178/PDF/2023-02-JWLD-01.pdf |
| _version_ | 1827926006069985280 |
|---|---|
| author | Amal Abo El-Magd Shaimaa M. Baraka Samir F.M. Eid |
| author_facet | Amal Abo El-Magd Shaimaa M. Baraka Samir F.M. Eid |
| author_sort | Amal Abo El-Magd |
| collection | DOAJ |
| description | Artificial neural network models (ANNs) were used in this study to predict reference evapotranspiration ( ETo) using climatic data from the meteorological station at the test station in Kafr El-Sheikh Governorate as inputs and reference evaporation values computed using the Penman–Monteith (PM) equation. These datasets were used to train and test seven different ANN models that included different combinations of the five diurnal meteorological variables used in this study, namely, maximum and minimum air temperature ( Tmax and Tmin), dew point temperature ( Tdw), wind speed ( u), and precipitation (P), how well artificial neural networks could predict ETo values. A feed- forward multi-layer artificial neural network was used as the optimization algorithm. Using the tansig transfer function, the final architected has a 6-5-1 structure with 6 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer that corresponds to the reference evapotranspiration. The root mean square error ( RMSE) of 0.1295 mm∙day –1 and the correlation coefficient ( r) of 0.996 are estimated by artificial neural network ETo models. When fewer inputs are used, ETo values are affected. When three separate variables were employed, the RMSE test values were 0.379 and 0.411 mm∙day –1 and r values of 0.971 and 0.966, respectively, and when two input variables were used, the RMSE test was 0.595 mm∙day –1 and the r of 0.927. The study found that including the time indicator as an input to all groups increases the prediction of ETo values significantly, and that including the rain factor has no effect on network performance. Then, using the Penman–Monteith method to estimate the missing variables by using the ETo calculator the normalised root mean squared error ( NRMSE) reached about 30% to predict ETo if all data except temperature is calculated, while the NRMSE reached about of 13.6% when used ANN to predict ETo using variables of temperature only. |
| first_indexed | 2024-03-13T05:32:18Z |
| format | Article |
| id | doaj.art-2aac4df4e0844e6ab436480c290c443f |
| institution | Directory Open Access Journal |
| issn | 2083-4535 |
| language | English |
| last_indexed | 2024-03-13T05:32:18Z |
| publishDate | 2023-05-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Journal of Water and Land Development |
| spelling | doaj.art-2aac4df4e0844e6ab436480c290c443f2023-06-14T15:14:58ZengPolish Academy of SciencesJournal of Water and Land Development2083-45352023-05-01No 5718https://doi.org/10.24425/jwld.2023.143768Using artificial neural networks to predict the reference evapotranspirationAmal Abo El-Magd0https://orcid.org/0000-0002-9579-4055Shaimaa M. Baraka1https://orcid.org/0000-0002-3157-8265Samir F.M. Eid2https://orcid.org/0000-0002-1220-9966Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre (ARC) Nadi El-Said St. Dokki, P.O. Box 256, Giza, EgyptAin Shams University, Faculty of Agriculture, Department of Agricultural Engineering, Cairo, EgyptAgricultural Engineering Research Institute (AEnRI), Agricultural Research Centre (ARC) Nadi El-Said St. Dokki, P.O. Box 256, Giza, EgyptArtificial neural network models (ANNs) were used in this study to predict reference evapotranspiration ( ETo) using climatic data from the meteorological station at the test station in Kafr El-Sheikh Governorate as inputs and reference evaporation values computed using the Penman–Monteith (PM) equation. These datasets were used to train and test seven different ANN models that included different combinations of the five diurnal meteorological variables used in this study, namely, maximum and minimum air temperature ( Tmax and Tmin), dew point temperature ( Tdw), wind speed ( u), and precipitation (P), how well artificial neural networks could predict ETo values. A feed- forward multi-layer artificial neural network was used as the optimization algorithm. Using the tansig transfer function, the final architected has a 6-5-1 structure with 6 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer that corresponds to the reference evapotranspiration. The root mean square error ( RMSE) of 0.1295 mm∙day –1 and the correlation coefficient ( r) of 0.996 are estimated by artificial neural network ETo models. When fewer inputs are used, ETo values are affected. When three separate variables were employed, the RMSE test values were 0.379 and 0.411 mm∙day –1 and r values of 0.971 and 0.966, respectively, and when two input variables were used, the RMSE test was 0.595 mm∙day –1 and the r of 0.927. The study found that including the time indicator as an input to all groups increases the prediction of ETo values significantly, and that including the rain factor has no effect on network performance. Then, using the Penman–Monteith method to estimate the missing variables by using the ETo calculator the normalised root mean squared error ( NRMSE) reached about 30% to predict ETo if all data except temperature is calculated, while the NRMSE reached about of 13.6% when used ANN to predict ETo using variables of temperature only.https://journals.pan.pl/Content/127178/PDF/2023-02-JWLD-01.pdfclimate dataeto calculatorfeed-forward artificial neural networkspenman–monteith methodreference evaporationroot mean squared error |
| spellingShingle | Amal Abo El-Magd Shaimaa M. Baraka Samir F.M. Eid Using artificial neural networks to predict the reference evapotranspiration Journal of Water and Land Development climate data eto calculator feed-forward artificial neural networks penman–monteith method reference evaporation root mean squared error |
| title | Using artificial neural networks to predict the reference evapotranspiration |
| title_full | Using artificial neural networks to predict the reference evapotranspiration |
| title_fullStr | Using artificial neural networks to predict the reference evapotranspiration |
| title_full_unstemmed | Using artificial neural networks to predict the reference evapotranspiration |
| title_short | Using artificial neural networks to predict the reference evapotranspiration |
| title_sort | using artificial neural networks to predict the reference evapotranspiration |
| topic | climate data eto calculator feed-forward artificial neural networks penman–monteith method reference evaporation root mean squared error |
| url | https://journals.pan.pl/Content/127178/PDF/2023-02-JWLD-01.pdf |
| work_keys_str_mv | AT amalaboelmagd usingartificialneuralnetworkstopredictthereferenceevapotranspiration AT shaimaambaraka usingartificialneuralnetworkstopredictthereferenceevapotranspiration AT samirfmeid usingartificialneuralnetworkstopredictthereferenceevapotranspiration |