Paper No. JAWRA-08-0070-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until October 1, 2009.
Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1
Article first published online: 25 MAR 2009
© 2009 American Water Resources Association
JAWRA Journal of the American Water Resources Association
Volume 45, Issue 2, pages 460–474, April 2009
How to Cite
Zhang, X., Srinivasan, R. and Van Liew, M. (2009), Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine. JAWRA Journal of the American Water Resources Association, 45: 460–474. doi: 10.1111/j.1752-1688.2009.00302.x
- Issue published online: 25 MAR 2009
- Article first published online: 25 MAR 2009
- Received April 17, 2008; accepted September 1, 2008.
- artificial neural network;
- computationally intensive;
- hydrologic modeling;
- soil and water assessment tool;
- support vector machine
Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross-validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3-fold cross-validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.