Stage and Discharge Forecasting by SVM and ANN Techniques

Abstract

In this study, forecasting of stage and discharge was done in a time-series framework across three time horizons using three models: (i) persistence model, (ii) feed-forward neural network (FFNN) model, and (iii) support vector machine (SVM) model. For these models, lagged values of the time series constituted the set of input variables which had been selected by principal component analysis (PCA). Parameters of FFNN and SVM models were determined by sensitivity analysis. All the three models were evaluated using data from Mahanadi River, India, and their forecasting performance was then compared. It is shown that over a shorter forecasting horizon, it is difficult to outperform the persistence model. Moreover, results show that forecasting of stage and discharge over a longer time frame by the SVM model is more accurate than that by the other two models.