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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: Badyalina, Basri, Shabri, Ani
Format: Article
Published: 2013
Subjects:
Q Science
Online Access:http://eprints.utm.my/id/eprint/40885/
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spelling my.utm.408852017-08-16T08:12:33Z http://eprints.utm.my/id/eprint/40885/ Streamflow forecasting at ungauged sites using multiple linear regression Badyalina, Basri Shabri, Ani Q Science 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. 2013 Article PeerReviewed Badyalina, Basri and Shabri, Ani (2013) Streamflow forecasting at ungauged sites using multiple linear regression. Matematika, 29 (1b). pp. 67-75. ISSN 0127-8274
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science
spellingShingle Q Science
Badyalina, Basri
Shabri, Ani
Streamflow forecasting at ungauged sites using multiple linear regression
description 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.
format Article
author Badyalina, Basri
Shabri, Ani
author_facet Badyalina, Basri
Shabri, Ani
author_sort Badyalina, Basri
title Streamflow forecasting at ungauged sites using multiple linear regression
title_short Streamflow forecasting at ungauged sites using multiple linear regression
title_full Streamflow forecasting at ungauged sites using multiple linear regression
title_fullStr Streamflow forecasting at ungauged sites using multiple linear regression
title_full_unstemmed Streamflow forecasting at ungauged sites using multiple linear regression
title_sort streamflow forecasting at ungauged sites using multiple linear regression
publishDate 2013
url http://eprints.utm.my/id/eprint/40885/
_version_ 1643650584497094656
score 13.214268

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