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

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-ye...

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Bibliographic Details
Published inWater (Basel) Vol. 14; no. 8; p. 1210
Main Authors Achite, Mohammed, Jehanzaib, Muhammad, Sattari, Mohammad Taghi, Toubal, Abderrezak Kamel, Elshaboury, Nehal, Wałęga, Andrzej, Krakauer, Nir, Yoo, Ji-Young, Kim, Tae-Woong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2022
Subjects
Algeria
ANN
Artificial intelligence
Climate
Evapotranspiration
FAO-56 Penman–Monteith
Gene expression
GEP
Humidity
Hydrologic cycle
Hydrology
Learning algorithms
Lower Cheliff
Machine learning
Meteorological data
Methods
Neural networks
Observational learning
Radial basis function
Radiation
reference evapotranspiration
Regression analysis
Root-mean-square errors
Semi arid areas
Support vector machines
Algeria
California
Serbia
United States > US
China
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Summary: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.
ISSN:2073-4441
2073-4441
DOI:10.3390/w14081210