Name
Abstract | ||
|---|---|---|
The GP, SVM-FFA, ANN and SVM-Wavelet modeling of ET0 was reported.SVM-Wavelet had the smallest RMSE of 0.233mmday-1 in testing phase.The ANN model had the largest RMSE of 0.450mmday-1.SVM-Wavelet model was found to perform better than the GP, SVM-FFA and ANN models. 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.compag.2015.02.010 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Soft computing,Forecasting,Firefly algorithm,Support vector machine,Wavelet,Serbia | Pearson product-moment correlation coefficient,Mean squared error,Control engineering,Genetic programming,Artificial intelligence,Soft computing,Artificial neural network,Statistical hypothesis testing,Correlation coefficient,Coefficient of determination,Engineering,Statistics,Machine learning | Journal |
Volume | Issue | ISSN |
113 | C | 0168-1699 |
Citations | PageRank | References |
11 | 1.05 | 16 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milan Gocic | 1 | 38 | 4.39 |
| Shervin Motamedi | 2 | 39 | 4.14 |
| Shahaboddin Shamshirband | 3 | 512 | 53.36 |
| Dalibor Petkovic | 4 | 11 | 1.05 |
| Sudheer Ch | 5 | 11 | 1.05 |
| Roslan Hashim | 6 | 38 | 3.76 |
| Muhammad Arif | 7 | 266 | 45.68 |