<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study introduces a new ANN updating procedure of streamflow prediction for a physically based HEC-HMS hydrological model of the Upper Thames River watershed (Ontario, Canada). Besides streamflow and precipitation, the updating procedure uses other meteorological variables as inputs, which are not applied in calibration of the HEC-HMS model. All the results of performance measures on training, validation and test datasets for river gauges at Mitchell and Stratford revealed that the ANN updated models have performed better than the HEC-HMS model. The ANN model results were in excellent agreement with observed streamflow. The uncertainties can be associated with different input variables and different length of datasets used in the HEC-HMS model and the ANN model. The performance results suggest improvement in the <italic>RMSE</italic> values of the trained networks when additional meteorological data was used. The updated errors from the gauged sites of Mitchell and Stratford were used to update the streamflow values at the ungauged site of JR750 of the HEC-HMS model. While the underlying physical process in the ANN model consisting of interconnected neurons to map input-output relationships is not easily understood (in a form of mathematical equation), the HEC-HMS hydrological model can reveal useful information about the parameters of a hydrological process.</p>
</abstract>

This study introduces a new ANN updating procedure of streamflow prediction for a physically based HEC-HMS hydrological model of the Upper Thames River watershed (Ontario, Canada). Besides streamflow and precipitation, the updating procedure uses other meteorological variables as inputs, which are not applied in calibration of the HEC-HMS model. All the results of performance measures on training, validation and test datasets for river gauges at Mitchell and Stratford revealed that the ANN updated models have performed better than the HEC-HMS model. The ANN model results were in excellent agreement with observed streamflow. The uncertainties can be associated with different input variables and different length of datasets used in the HEC-HMS model and the ANN model. The performance results suggest improvement in the RMSE values of the trained networks when additional meteorological data was used. The updated errors from the gauged sites of Mitchell and Stratford were used to update the streamflow values at the ungauged site of JR750 of the HEC-HMS model. While the underlying physical process in the ANN model consisting of interconnected neurons to map input-output relationships is not easily understood (in a form of mathematical equation), the HEC-HMS hydrological model can reveal useful information about the parameters of a hydrological process.

Output updating of a physically based model for gauged and ungauged sites of the Upper Thames River watershed

Department of Water and Environmental Engineering, Faculty of Civil Engineering

Department of Civil and Environmental Engineering

University of Western Ontario, Spencer Engineering Building

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This study introduces a new ANN updating procedure of streamflow prediction for a physically based HEC-HMS hydrological model of the Upper Thames River...

Output updating of a physically based model for gauged and ungauged sites of the Upper Thames River watershed

Published Online: Aug 10, 2023

Page range: 259 - 270

Received: Jun 02, 2022

Accepted: Apr 24, 2023

DOI: https://doi.org/10.2478/johh-2023-0019

Keywords
Neural network, Bayesian, Hydrometeorology, Hydrological model, HEC-HMS

© 2023 Ponselvi Jeevaragagam et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Output updating of a physically based model for gauged and ungauged sites of the Upper Thames River watershed

Published Online: Aug 10, 2023

Page range: 259 - 270

Received: Jun 02, 2022

Accepted: Apr 24, 2023

DOI: https://doi.org/10.2478/johh-2023-0019

KeywordsNeural network, Bayesian, Hydrometeorology, Hydrological model, HEC-HMS

© 2023 Ponselvi Jeevaragagam et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Neural network, Bayesian, Hydrometeorology, Hydrological model, HEC-HMS