Abstract
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.
Highlights
Flood routing is important in the design of flood protection measures in order to estimate how the proposed measures will affect the behavior of flood waves in rivers so that adequate protection and economic solutions can be found [1]
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
The Wilson flood data were modeled by Karahan et al (2014) using a nonlinear Muskingum model incorporating lateral flow (NLMM-L) and SSE value was found as 9.823
Summary
Flood routing is important in the design of flood protection measures in order to estimate how the proposed measures will affect the behavior of flood waves in rivers so that adequate protection and economic solutions can be found [1]. A great deal of studies based on hydraulic models was developed by various researchers for flood routing [2,3,4,5,6,7,8] These models require the measurement of flow depth and discharges. If detailed topographical surveys of channel cross-sections and roughness at close intervals are not available, hydraulic models are not suitable to serve the purpose of flood routing In this case, hydrologic models may be used because they can cope with sparse spatial data [9]. The fact that the whole observed data has been used for determining the model parameters in the abovementioned heuristic methods constitutes the major disadvantage of these approaches This may lead to overtraining problem that degrades generalization capability [34].
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