Streamflow Forecasting at Ungaged Sites Using Support Vector Machines

Developing reliable estimates of streamflow prediction are crucial for water resources management and flood forecasting purposes. The objectives of this study are to investigate the potential of support vector machines (SVM) model for streamflow forecasting at ungaged sites, and to compare its performance with other statistical method of multiple linear regression (MLR). Three quantitative standard statistical indices such as mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe coefficient of efficiency (CE) are employed to validate both models. The performances of both models are assessed by forecasting annual maximum flow series from 88 water level stations in Peninsular Malaysia. Based on these results, it was found that the SVM model outperforms the prediction ability of the traditional MLR model under all of the designated return periods.