| Title |
Comparison of Different Multiple Linear Regression Models for Real-time Flood Stage Forecasting |
| Authors |
최승용(Choi, Seung Yong) ; 한건연(Han, Kun Yeun) ; 김병현(Kim, Byung Hyun) |
| DOI |
https://doi.org/10.12652/Ksce.2012.32.1B.009 |
| Keywords |
홍수위 예측;다중선형회귀;자료지향형;회귀계수 산정 방법 flood stage forecasting;multiple linear regression model;data-driven;regression coefficient estimation methods |
| 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. |