Modern Techniques to Modeling Reference Evapotranspiration in a Semiarid Area Based on ANN and GEP Models

• Main Authors: Achite, M. (Author), Elshaboury, N. (Author), Jehanzaib, M. (Author), Kim, T.-W (Author), Krakauer, N. (Author), Sattari, M.T (Author), Toubal, A.K (Author), Wałęga, A. (Author), Yoo, J.-Y (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Algeria; ANN; artificial neural network; evapotranspiration; FAO-56 penman–monteith; FAO-56 Penman–Monteith; Gene expression; Gene-expression programming; GEP; hydrological cycle; Low cheliff; Lower Cheliff; Machine learning; Mean square error; Meteorological station; Meteorology; Penman Monteith; Penman-Monteith equation; Programming models; Radial basis function networks; reference evapotranspiration; Reference evapotranspiration; Root mean square errors
• Online Access: View Fulltext in Publisher

 Evapotranspiration (ET) is a significant aspect of the hydrologic cycle, notably in irrigated agriculture. Direct approaches for estimating reference evapotranspiration (ET0) are either difficult or need a large number of inputs that are not always available from meteorological stations. Over a 6-year period (2006–2011), this study compares Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN), and Gene Expression Programming (GEP) machine learning approaches for estimating daily ET0 in a meteorological station in the Lower Cheliff Plain, northwest Algeria. ET0 was estimated using the FAO-56 Penman–Monteith (FAO56PM) equation and observed meteorological data. The estimated ET0 using FAO56PM was then used as the target output for the machine learning models, while the observed meteorological data were used as the model inputs. Based on the coefficient of determination (R2), root mean square error (RMSE), and Nash–Sutcliffe efficiency (EF), the RBFNN and GEP models showed promising performance. However, the FFNN model performed the best during training (R2 = 0.9903, RMSE = 0.2332, and EF = 0.9902) and testing (R2 = 0.9921, RMSE = 0.2342, and EF = 0.9902) phases in forecasting the Penman–Monteith evapotranspiration. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.