Browse > Article
http://dx.doi.org/10.3741/JKWRA.2004.37.1.067

Dam Inflow Forecasting for Short Term Flood Based on Neural Networks in Nakdong River Basin  

Yoon, Kang-Hoon (한국건설기술연구원 수자원연구부)
Seo, Bong-Cheol (한국건설기술연구원 수자원연구부)
Shin, Hyun-Suk (부산대학교 토목공학과)
Publication Information
Journal of Korea Water Resources Association / v.37, no.1, 2004 , pp. 67-75 More about this Journal
Abstract
In this study, real-time forecasting model(Neural Dam Inflow Forecasting Model; NDIFM) based on neural network to predict the dam inflow which is occurred by flood runoff is developed and applied to check its availability for the operation of multi-purpose reservoir Developed model Is applied to predict the flood Inflow on dam Nam-Gang in Nak-dong river basin where the rate of flood control dependent on reservoir operation is high. The input data for this model are average rainfall data composed of mean areal rainfall of upstream basin from dam location, observed inflow data, and predicted inflow data. As a result of the simulation for flood inflow forecasting, it is found that NDIFM-I is the best predictive model for real-time operation. In addition, the results of forecasting used on NDIFM-II and NDIFM-III are not bad and these models showed wide range of applicability for real-time forecasting. Consequently, if the quality of observed hydrological data is improved, it is expected that the neural network model which is black-box model can be utilized for real-time flood forecasting rather than conceptual models of which physical parameter is complex.
Keywords
Dam inflow forecasting; Neural Network; Back-propagation; Flood Runoff;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 신현석 (1998). '인공 신경망의 수자원 및 환경 분야에의 응용,' 한국수자원학회지, 한국수자원학회, 제31권, 제1호, pp. 97-103   과학기술학회마을
2 신현석, 최시중, 김중훈 (1998). '신경망을 이용한 도시유역 유출 및 비점원 오염물 배출 모형화 연구,' 대한토목학회 논문집, 대한토목학회, 제18권, 제II-5호, pp. 437-438   과학기술학회마을
3 건설교통부 (2001), 지능형 신경망 모형을 적용한 낙동강 홍수예보시스템 개선
4 신현석, 박무종 (1999a). '신경망을 이용한 우리나라의 시공간적 가뭄의 해석,' 한국수자원학회 논문집, 한국수자원학회, 제32권, 제1호, pp. 15-29   과학기술학회마을
5 안경수, 김주환 (1998). '신경회로망을 이용한 유출수문곡선 모의에 관한 연구,' 한국수자원학회 논문집, 한국수자원학회, 제31권, 제1호, pp. 13-25   과학기술학회마을
6 신현석, 박무종 (1999b). '신경망 기법을 이용한 연평균 강우량의 공간 해석,' 한국수자원학회 논문집, 한국수자원학회, 제32권, 제1호, pp. 3-13   과학기술학회마을
7 Jason Smith and Robert N. Eil (1995). 'Neural-network models of rainfall-runoff process,' Journal of Water Resources Planning and Management, Vol. 121, No. 6, pp. 499-508   DOI   ScienceOn
8 심순보, 김만식, 심규철 (1998). '신경망 이론에 의한 다목적 저수지의 홍수유입량 예측,' 한국수자원학회 논문집, 한국수자원학회, 제31권, 제1호, pp. 45-57   과학기술학회마을
9 Cameron M. Zealand, Donald H. Burn and Slobodan P. Simonovic (1999). 'Short term streamflow forecasting using artificial neural networks,' Journal of Hydrology, Vol. 214, pp. 32-48   DOI   ScienceOn
10 안상진, 연인성, 한양수, 이재경 (2001a). '신경망 모형을 적용한 금강 공주지점의 수질예측,' 한국수자원학회 논문집, 한국수자원학회, 제34권, 제6호, pp. 701-711   과학기술학회마을
11 Hyun-Suk Shin and Jose D. Salas (2000). 'Regional drought analysis based on neural networks,' Journal of Hydrologic Engineering, Vol. 5, No. 2, pp. 145-155   DOI   ScienceOn
12 P.J. Werbos (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis, Harvard University, Cambridge, MA
13 Marina Campolo, Paolo Andreussi, and Alfredo Soldati (1999). 'River flood forecasting with a neural network model,' Water Resources Research, Vol. 35, No. 4, pp. 1191-1197   DOI   ScienceOn
14 안상진, 전계원 (2001b). 'RBF를 이용한 홍수유출량 예측,' 대한토목학회 논문집, 대한토목학회, 제21권, 제6-B호, pp. 599-607
15 N. Sajikumar and B. S. Thandaveswara (1999). 'A non-linear rainfall-runoff model using an artificial neural network,' Journal of Hydrology, Vol. 216, pp. 32-55   DOI   ScienceOn
16 Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). 'Learning internal representations by error back propagation,' Parallel distributed processing, Edited by Rumelhart, D.E., McCelland, J.L. and PDP Research Group, Chapter 8, Cambridge, MA, MIT Press