Research Article
A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods
Table 3
Performance comparison of the SVM, SOM-SVM1, and SOM-SVM2 models for 1 h ahead forecasts using RRMSE and CE as criteria.
| Typhoon events | SOM | SOM-SVM1 | SOM-SVM2 | RRMSE | CE | RRMSE | CE | RRMSE | CE |
| Polly | 1.358 | 0.583 | 0.448 | 0.912 | 0.374 | 0.916 | Ted | 0.316 | 0.770 | 0.157 | 0.941 | 0.128 | 0.958 | Tim | 3.627 | 0.551 | 1.724 | 0.873 | 1.253 | 0.901 | Fred | 1.460 | 0.745 | 0.369 | 0.960 | 0.365 | 0.952 | Gladys | 7.430 | 0.644 | 1.354 | 0.921 | 0.545 | 0.917 | Seth | 5.882 | 0.650 | 0.817 | 0.936 | 0.731 | 0.950 | Herb | 1.375 | 0.959 | 0.367 | 0.967 | 0.268 | 0.975 | Zeb | 0.340 | 0.905 | 0.187 | 0.958 | 0.186 | 0.955 | Xangsane | 0.280 | 0.918 | 0.187 | 0.915 | 0.151 | 0.936 | Nari | 0.268 | 0.808 | 0.263 | 0.895 | 0.222 | 0.925 | Haiyan | 1.222 | 0.743 | 0.471 | 0.912 | 0.337 | 0.940 | Rammasun | 2.975 | 0.643 | 0.740 | 0.929 | 0.613 | 0.929 | Aere | 2.784 | 0.935 | 1.245 | 0.979 | 1.057 | 0.988 | Nock-Ten | 0.919 | 0.788 | 0.769 | 0.961 | 0.657 | 0.937 | Nanmadol | 1.681 | 0.806 | 0.258 | 0.924 | 0.237 | 0.908 | Haitang | 7.049 | 0.727 | 2.329 | 0.923 | 1.225 | 0.948 | Matsa | 4.292 | 0.523 | 1.474 | 0.904 | 1.032 | 0.900 | Talim | 3.623 | 0.874 | 0.874 | 0.883 | 0.779 | 0.958 | Krosa | 0.667 | 0.960 | 0.554 | 0.980 | 0.402 | 0.981 | Fung-Wong | 0.876 | 0.830 | 0.286 | 0.979 | 0.262 | 0.980 | Sinlaku | 0.696 | 0.858 | 0.212 | 0.939 | 0.226 | 0.913 | Jangmi | 0.400 | 0.937 | 0.133 | 0.986 | 0.132 | 0.978 |
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