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

• Published in: Meteorology and atmospheric physics 2012-11, Vol.118 (3-4), p.163-178
• Main Authors: Ladlani, Ibtissem, Houichi, Larbi, Djemili, Lakhdar, Heddam, Salim, Belouz, Khaled
• Format: Article
• Language: English
• Subjects: Air temperature; Algeria; Aquatic Pollution; Atmospheric Sciences; Climatic data; Comparative studies; Correlation coefficient; Earth and Environmental Science; Earth Sciences; Estimates; Evapotranspiration; Hydrologic cycle; Hydrology; Irrigation; Irrigation design; Math. Appl. in Environmental Science; Mathematical analysis; Mathematical models; Meteorology; Neural networks; Original Paper; Radial basis function; Regression; Relative humidity; Terrestrial Pollution; Waste Water Technology; Water Management; Water Pollution Control; Water resources management; Wind speed
• ISSN: 0177-7971; 1436-5065
• DOI: 10.1007/s00703-012-0205-9

Estimation of reference evapotranspiration (ET 0 ) 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 ET 0 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 ET 0 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.