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Journal of Applied Mathematics
Volume 2014, Article ID 714213, 7 pages
Research Article

River Flow Estimation from Upstream Flow Records Using Support Vector Machines

1Department of Civil Engineering, Faculty of Engineering, Pamukkale University, 20070 Denizli, Turkey
2Department of Electrical and Electronics Engineering, Faculty of Engineering, Pamukkale University, 20070 Denizli, Turkey

Received 28 January 2014; Revised 22 May 2014; Accepted 6 June 2014; Published 30 June 2014

Academic Editor: Guohe Huang

Copyright © 2014 Halil Karahan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel architecture for flood routing model has been proposed and its efficiency is validated on several problems by employing support vector machines. The architecture is designed by including the inputs and observed and calculated outflows from the previous time step output. Whole observed data have been used for determining the model parameters in the heuristic methods given in the literature, which constitutes the major disadvantage of the existing approaches. Moreover, using the whole data for training may lead to overtraining problem that causes overfitting of estimations and data. Therefore, in this study, 60–90% of the data are randomly selected for training and then the remaining data are used for validation. In order to take the effects of the measurement errors into consideration, the data are corrupted by some additive noise. The results show that the proposed architecture improves the model performance under noisy and missing data conditions and that support vector machines can be powerful alternative in flood routing modeling.