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Soft computing approaches for forecasting reference evapotranspiration

Gocić, Milan, Motamedi, Shervin, Shamshirband, Shahaboddin, Petković, Dalibor, Ch, Sudheer, Hashim, Roslan, Arif, Muhammad
Computers and electronics in agriculture 2015 v.113 pp. 164-173
agricultural resources, algorithms, correlation, data collection, equations, evapotranspiration, neural networks, planning, prediction, water resources, Serbia
Accurate estimation of reference evapotranspiration (ET0) is needed for planning and managing water resources and agricultural production. The FAO-56 Penman–Monteith equation is used to determinate ET0 based on the data collected during the period 1980–2010 in Serbia. In order to forecast ET0, four soft computing methods were analyzed: genetic programming (GP), support vector machine-firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine–wavelet (SVM–Wavelet). The reliability of these computational models was analyzed based on simulation results and using five statistical tests including Pearson correlation coefficient, coefficient of determination, root-mean-square error, absolute percentage error, and mean absolute error. The end-point result indicates that SVM–Wavelet is the best methodology for ET0 prediction, whereas SVM–Wavelet and SVM-FFA models have higher correlation coefficient as compared to ANN and GP computational methods.