Using artificial neural networks to predict the reference evapotranspiration

• Autorzy: Abo El-Magd Amal , Baraka Shaimaa M. , Eid Samir F.M.

Identyfikatory

DOI

10.24425/jwld.2023.143768

• Języki publikacji: EN

Abstrakty

EN

Artificial neural network models (ANNs) were used in this study to predict reference evapotranspiration (ET_o) 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 ET_o 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 ET_o models. When fewer inputs are used, ET_o 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 ET_o 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 ET_o calculator the normalised root mean squared error (NRMSE) reached about 30% to predict ET_o if all data except temperature is calculated, while the NRMSE reached about of 13.6% when used ANN to predict ET_o using variables of temperature only.

Słowa kluczowe

EN

climate data; ETo calculator; feedforward artificial neural networks; Penman-Monteith method; reference evaporation; root mean square error

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2023
• Tom: no. 57
• Strony: 1--8
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Abo El-Magd Amal

• Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre (ARC) Nadi El-Said St. Dokki, P.O. Box 256, Giza, Egypt

• https://orcid.org/0000-0002-9579-4055

autor

Baraka Shaimaa M.

• Ain Shams University, Faculty of Agriculture, Department of Agricultural Engineering, Cairo, Egypt

• https://orcid.org/0000-0002-3157-8265

autor

Eid Samir F.M.

• Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre (ARC) Nadi El-Said St. Dokki, P.O. Box 256, Giza, Egypt

• https://orcid.org/0000-0002-1220-9966

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).