EC-SVM approach for real-time hydrologic forecasting
This study demonstrates a combined application of chaos theory and support vector machine (SVM) in the analysis of chaotic time series with a very large sample data record. A large data record is often required and causes computational difficulty. The decomposition method is used in this study to circumvent this difficulty. The various parameters inherent in chaos technique and SVM are. optimised, with the assistance of an evolutionary algorithm, to yield the minimal prediction error The performance of the proposed scheme, EC-SVM, is demonstrated on two daily runoff time series: of Tryggevaelde catchment, Denmark and the Mississippi River at Vicksburg. The prediction accuracy the proposed scheme is compared with that of the conventional approach and the recently introduced inverse approach. This comparison shows that EC-SVM yields a significantly lower normalised RMSE value of 0.347 for the Tryggevaelde catchment runoff and 0.0385 for the Mississippi River flow compared to 0.444 and 0.2064, respectively, resulting from the conventional approach. A slight improvement in accuracy was obtained by analysing the first difference or the daily flow difference time series. it should be noted, however, that the computational speed in analysing the daily flow difference time series is significantly much faster than that of the daily flow time series.
support vector machine decomposition method evolutionary algorithm chaotic technique