Integrating Support Vector Regression and a geomorphologic Artificial Neural Network for daily rainfall-runoff modeling

Abstract

In spite of the efficiency of the Artificial Neural Networks (ANNs) for modeling nonlinear and complicated rainfall-runoff (R-R) process, they suffer from some drawbacks. Support Vector Regression (SVR) model has appeared to be a powerful alternative to reduce some of these drawbacks while retaining many strengths of ANNs. In this paper, to form a new rainfall-runoff model called SVR-GANN, a SVR model is combined with a geomorphologic-based ANN model. The GANN is a three-layer perceptron model, in which the number of hidden neurons is equal to the number of possible flow paths within a watershed and the connection weights between hidden layer and output layer are specified by flow path probabilities which are not updated during the training process. The capabilities of the proposed SVR-GANN model in simulating the daily runoff is investigated in a case study of three sub-basins located in a semi-arid region in Iran. The results of the proposed model are compared with those of ANN-based back propagation algorithm (ANN-BP), traditional SVR, ANN-based genetic algorithm (ANN-GA), adaptive neuro-fuzzy inference system (ANFIS), and GANN from the standpoints of parsimony, equifinality, robustness, reliability, computational time, simulation of hydrograph ordinates (peak flow, time to peak, and runoff volume) and also saving the main statistics of the observed data. The results show that prediction accuracy of the SVR-GANN model is usually better than those of ANN-based models and the proposed model can be applied as a promising, reliable, and robust prediction tool for rainfall-runoff modeling.