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Parameter Uncertainty in HEC-RAS 1D CSU Scour Model


Affiliations
1 Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India
 

The predictive capability of a model is dependent on the parameter uncertainty involved in it. This study examines the effect of predictive uncertainty and parameter sensitivity in the application of the well-known HEC-RAS 1D hydrodynamic CSU (Colorado State University) scour prediction model. Correlation-based technique was used for carrying out the sensitivity analysis. Monte Carlo method was adopted for uncertainty quantification. The methodology suggested in the present study drastically improved the predictive capability of the model, by reducing the model error from 26.6% to 0.07%. In general, it improved the predictive capability of any scour model when tested on 19 datasets.

Keywords

Hydrodynamic Model, Parameter Uncertainty, Scour, Prediction, Sensitivity Analysis.
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  • Parameter Uncertainty in HEC-RAS 1D CSU Scour Model

Abstract Views: 346  |  PDF Views: 123

Authors

Praveen Rathod
Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India
V. L. Manekar
Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India

Abstract


The predictive capability of a model is dependent on the parameter uncertainty involved in it. This study examines the effect of predictive uncertainty and parameter sensitivity in the application of the well-known HEC-RAS 1D hydrodynamic CSU (Colorado State University) scour prediction model. Correlation-based technique was used for carrying out the sensitivity analysis. Monte Carlo method was adopted for uncertainty quantification. The methodology suggested in the present study drastically improved the predictive capability of the model, by reducing the model error from 26.6% to 0.07%. In general, it improved the predictive capability of any scour model when tested on 19 datasets.

Keywords


Hydrodynamic Model, Parameter Uncertainty, Scour, Prediction, Sensitivity Analysis.

References





DOI: https://doi.org/10.18520/cs%2Fv118%2Fi8%2F1227-1234