지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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In this study, Mike11 will be used as the numerical model where a data assimilation method will be applied to it. This paper aims to gain an insight and understanding of data assimilation in flood forecasting models.
It will start with a general discussion of data assimilation, followed by a description of the methodology and discussion of the statistical error forecast model used, which in this case is the linear regression. This error forecast model is applied to the water level forecast simulated by MIKE11 to produced improved forecast and validated against real measurements. It is found that there exists a phase error in the improved forecasts. Hence, 2 general formula are used to account for this phase error and they have shown improvement to the accuracy of the forecasts, where one improved the immediate forecast of up to 5 hours while the other improved the estimation of the peak discharge.
목차
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참고문헌 (4)
참고문헌 신청
/ 2008 / MIKE 11 Reference Manual, DHI Water, Environment and Health
Babovic, V / 2001 / Neural networks as routine for error updating of numerical models / Journal of Hydraulic Engineering, ASCE 127 (3) : 181 ~ 193
Morten. R / 1989 / Improvement of updating routine in MIKE11 modelling system for real-time flood forecasting
Refsgaard, J.C / 1997 / Validating and intercomparision of different updating procedures for real-time forecasting / Nordic Hydrology (28) : 65 ~ 84
Babovic, V / 2001 / Neural networks as routine for error updating of numerical models / Journal of Hydraulic Engineering, ASCE 127 (3) : 181 ~ 193
Morten. R / 1989 / Improvement of updating routine in MIKE11 modelling system for real-time flood forecasting
Refsgaard, J.C / 1997 / Validating and intercomparision of different updating procedures for real-time forecasting / Nordic Hydrology (28) : 65 ~ 84
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