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Meteorology and Atmospheric Physics

, Volume 118, Issue 3–4, pp 163–178 | Cite as

Modeling daily reference evapotranspiration (ET0) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): a comparative study

  • Ibtissem Ladlani
  • Larbi Houichi
  • Lakhdar Djemili
  • Salim Heddam
  • Khaled Belouz
Original Paper

Abstract

Estimation of reference evapotranspiration (ET0) is needed to support irrigation design and scheduling, and watershed hydrology studies. There are many available methods to estimate evapotranspiration from a water surface, comprising both direct and indirect methods. In the first part of this study, the generalized regression neural networks model (GRNN) and radial basis function neural network (RBFNN) are developed and compared in order to estimate the reference ET0 for the first time in Algeria. Various daily climatic data, that is, daily mean relative humidity, sunshine duration, maximum, minimum and mean air temperature, and wind speed from Dar El Beida, Algiers, Algeria, are used as inputs to the GRNN and RBFNN models to estimate the ET0 obtained using the FAO-56 Penman-Monteith equation (PM56). The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. In the second part of the study, the empirical Hargreaves-Samani (HG) and Priestley-Taylor (PT) equations are also considered for the comparison. Based on the comparisons, the GRNN was found to perform better than the RBFNN, Priestley-Taylor and Hargreaves-Samani models. The RBFNN model is ranked as the second best model.

Keywords

Root Mean Square Error Artificial Neural Network Model Radial Basis Function Neural Network Generalize Regression Neural Network Mean Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Ibtissem Ladlani
    • 1
  • Larbi Houichi
    • 2
  • Lakhdar Djemili
    • 1
  • Salim Heddam
    • 3
  • Khaled Belouz
    • 4
  1. 1.Hydraulics Department, Faculty of Engineering SciencesUniversity Badji-Mokhtar AnnabaAnnabaAlgeria
  2. 2.Hydraulics Department, Institute of Civil Engineering-Hydraulics and ArchitectureUniversity Hadj Lakhdar BatnaBatnaAlgeria
  3. 3.Hydraulics Division, Agronomy Department, Faculty of ScienceUniversity 20 Août 1955SkikdaAlgeria
  4. 4.Hydraulics DepartmentSuperior National School of Agronomics (ENSA)El HarrachAlgeria

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