Streamflow forecasting at ungauged sites using multiple linear regression
Developing reliable estimates of streamflow prediction are crucial for water resources management and flood forecasting purposes. The objectives of this study are to identifying which the physiographical and hydrological characteristics affected in multiple linear regressions (MLR) model to estimate...
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| Main Authors: | , |
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| Format: | Article |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/40885/ |
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| Summary: | Developing reliable estimates of streamflow prediction are crucial for water resources management and flood forecasting purposes. The objectives of this study are to identifying which the physiographical and hydrological characteristics affected in multiple linear regressions (MLR) model to estimated flood quantile at ungauged site. MLR model is applied to 70 catchments located in the province of Peninsular Malaysia. Three quantitative standard statistical indices such as mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe coefficient of efficiency (CE) are employed to validate models. MLR model are built separately to estimate flood quantile for T=10 years and T=100 years. The results indicate that elevation, longest drainage path and slope were the best input for MLR model. |
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