Improvement of streamflow simulation for gauge site of hydrological model
The paper presents an improvement procedure for streamflow simulation at gauged site of a semi-distributed river basin model. In addition to streamflow and precipitation, meteorological observations that are not employed in the HEC-HMS model calibration are used as inputs in the procedure. Some of t...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/40356/1/PonselviJeevaragagam2013_ImprovementofStreamflowSimulationforGauged.pdf http://eprints.utm.my/id/eprint/40356/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:125977 |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | The paper presents an improvement procedure for streamflow simulation at gauged site of a semi-distributed river basin model. In addition to streamflow and precipitation, meteorological observations that are not employed in the HEC-HMS model calibration are used as inputs in the procedure. Some of the available meteorological variables may be of limited values in calibrating a large range of streamflow hydrographs for obtaining the optimum state variables and parameters of a river basin model. This study presents the integration of the Bayesian regularization neural network with the HEC-HMS model to provide most accurate streamflow simulations at gauged site, for a wide range of streamflow hydrographs pertinent to the hydrometeorological conditions. The artificial neural network is capable of generating a good generalization with given hydrometeorological patterns. |
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