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http://dx.doi.org/10.12652/Ksce.2012.32.1B.009

Comparison of Different Multiple Linear Regression Models for Real-time Flood Stage Forecasting  

Choi, Seung Yong (국립방재연구원)
Han, Kun Yeun (경북대학교 공과대학 건축.토목공학부)
Kim, Byung Hyun (캘리포니아주립대학교 얼바인 & 수문모델링센터)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.32, no.1B, 2012 , pp. 9-20 More about this Journal
Abstract
Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.
Keywords
flood stage forecasting; multiple linear regression model; data-driven; regression coefficient estimation methods;
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Times Cited By KSCI : 3  (Citation Analysis)
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