Soft computing approaches for forecasting reference evapotranspiration

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@Article{Gocic:2015:CEA,

• author = "Milan Gocic and Shervin Motamedi and Shahaboddin Shamshirband and Dalibor Petkovic and Sudheer Ch and Roslan Hashim and Muhammad Arif",
• title = "Soft computing approaches for forecasting reference evapotranspiration",
• journal = "Computers and Electronics in Agriculture",
• volume = "113",
• pages = "164--173",
• year = "2015",
• ISSN = "0168-1699",
• DOI = "doi:10.1016/j.compag.2015.02.010",
• URL = "http://www.sciencedirect.com/science/article/pii/S0168169915000526",
• abstract = "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 analysed: 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 analysed 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.",
• keywords = "genetic algorithms, genetic programming, Soft computing, Forecasting, Firefly algorithm, Support vector machine, Wavelet, Serbia",

}

Genetic Programming entries for Milan Gocic Shervin Motamedi Shahaboddin Shamshirband Dalibor Petkovic Sudheer Ch Roslan Hashim Muhammad Arif Syed Hamid

Citations