A wavelet-support vector machine conjunction model for monthly streamflow forecasting

The study investigates the accuracy of wavelet and support vector machine conjunction model in monthly streamflow forecasting. The conjunction method is obtained by combining two methods, discrete wavelet transform and support vector machine, and compared with the single support vector machine. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in Eastern Black Sea region of Turkey are used in the study. The root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) statistics are used for the comparing criteria. The comparison of results reveals that the conjunction model could increase the forecast accuracy of the support vector machine model in monthly streamflow forecasting. For the Gerdelli and Isakoy stations, it is found that the conjunction models with RMSE = 13.9 m(3)/s, MAE = 8.14 m(3)/s, R = 0.700 and RMSE = 8.43 m(3)/s, MAE = 5.62 m(3)/s, R = 0.768 in test period is superior in forecasting monthly streamflows than the most accurate support vector regression models with RMSE = 15.7 m(3)/s, MAE = 10 m(3)/s, R = 0.590 and RMSE = 11.6 m(3)/s, MAE = 7.74 m(3)/s, R = 0.525, respectively. (C) 2011 Elsevier B.V. All rights reserved.