If you are an ASABE member or if your employer has arranged for access to the full-text, click the underlined title below to view.

Don't have access to the full text? Click here for options.

Physics-based models for simulation of runoff and sediment yield from watersheds are quite complex and involved due to tremendous spatial variability of watershed characteristics and precipitation patterns. Recently, pattern-learning algorithms such as the artificial neural networks (ANN) have gained popularity in simulating the rainfall-runoff-sediment yield processes producing comparable accuracy. We have simulated daily, weekly, and monthly runoff and sediment yield from an Indian watershed (area= 7820 Sq.Km), with data from the monsoon period, using support vector machines (SVM), a statistical learning theory based pattern-learning algorithm. The performance of the model was evaluated using correlation coefficient (r) and coefficient of efficiency (E). The time series data was split into a training set for the learning process and a prediction set for comparison of the model’s forecasting ability. The results of SVM were compared to those of ANN. We concluded that SVM provided significant improvement in both training and prediction abilities as compared to those of ANN. ANN being a computationally intensive method, SVM could be used as an efficient alternative for runoff and sediment yield predictions providing at least comparable accuracy.