Performance assessment of artificial neural networks and support vector regression models for stream flow predictions

Water resources planning, development, and management need reliable forecasts of river flows. In past few decades, an important dimension has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence-based modeling. In this paper, the performance of three artificial neural network (ANN) and four support vector regression (SVR) models was investigated to predict streamflows in the Upper Indus River. Results from ANN models using three different optimization techniques, namely Broyden-Fletcher-Goldfarb-Shannon, Conjugate Gradient, and Back Propagation algorithms, were compared with one another. A further comparison was made between these ANNs and four types of SVR models which were based on linear, polynomial, radial basis function, and sigmoid kernels. Past 30 years' monthly data for precipitation, temperature, and streamflow obtained from Pakistan Surface Water Hydrology Department Lahore were used for this purpose. Three types of input combinations with respect to the main input variables (temperature, precipitation, and stream flow) and several types of input combinations with respect to time lag were tested. The best input for ANN and SVR models was identified using correlation coefficient analysis and genetic algorithm. The performance of the ANN and SVR models was evaluated by mean bias error, Nash-Sutcliffe efficiency, root mean square error, and correlation coefficient. The efficiency of the Broyden-Fletcher-Goldfarb-Shannon-ANN model was found to be much better than that of other models, while the SVR model based on radial basis function kernel predicted stream flows with comparatively higher accuracy than the other kernels. Finally, long-term predictions of streamflow have been made by the best ANN model. It was found that stream flow of Upper Indus River has a decreasing trend.