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http://dx.doi.org/10.3741/JKWRA.2004.37.2.145

Real-Time Forecasting of Flood Runoff Based on Neural Networks in Nakdong River Basin & Application to Flood Warning System  

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.2, 2004 , pp. 145-154 More about this Journal
Abstract
The purpose of this study is to develop a real-time forecasting model in order to predict the flood runoff which has the nature of non-linearity and to verify applicability of neural network model for flood warning system. Developed model based on neural network, NRDFM(Neural River Discharge-Stage Forecasting Model) is applied to predict the flood discharge on Waekwann and Jindong stations in Nakdong river basin. As a result of flood forecasting on these two stations, it can be concluded that NRDFM-II is the best predictive model for real-time operation. In addition, the results of forecasting used on NRDFM-I and NRDFM-II model are not bad and these models showed sufficient probability for real-time flood forecasting. Consequently, it is expected that NRDFM in this study can be utilized as suitable model for real-time flood warning system and this model can perform flood control and management efficiently.
Keywords
Neural Network; Back-propagation; Rainfall-Runoff; Flood Forecasting; Flood Warning System;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 건설교통부 낙동강홍수통제소 (2000). 낙동강홍수예경보
2 김주환 (1993). 신경회로망을 이용한 하천유출량의 수문학적 예측에 관한 연구, 박사학위논문, 인하대학교
3 Box, G. E. P., and G. M. Jenkins (1976). Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco
4 건설교통부 (2001). 지능형 신경망 모형을 적용한 낙동강 홍수예보시스템 개선
5 심순보, 김만식, 심규철 (1998). '신경망 이론에 의한 다목적 저수지의 홍수유입량 예측', 한국수자원학회논문집, 한국수자원학회, 제31권, 제1호, pp. 45-57
6 안경수, 김주환 (1998). '신경회로망을 이용한 유출수문곡선 모의에 관한 연구', 한국수자원학회논문집, 한국수자원학회, 제31권, 제1호, pp. 13-25   과학기술학회마을
7 안상진, 전계원 (2001). 'RBF를 이용한 홍수유출량 예측', 대한토목학회 논문집, 대한토목학회, 제21권, 제6-B호, pp. 599-607   과학기술학회마을
8 이관수, 박성천, 이한민, 진영훈 (2000). '인공신경망 이론의 B.P 알고리즘을 적용한 영산강의 유출량 예측에 관한 연구', 대한토목학회 논문집, 대한토목학회, 제20권, 제5-B호, pp. 679-688   과학기술학회마을
9 Burnash, R. J., R. L. Ferral, and R. A. McGuire (1973). A generalized stream flow simulation system in Conceptual Modeling for Digital Computers, U.S. National Weather Service, Sacramento, Calif.
10 Asaad Y. Shamseldin (1997). 'Application of a neural network technique to rainfall-runoff modelling', Journal of Hydrology, Vol. 199, pp. 272-294   DOI   ScienceOn
11 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
12 Crawford, N. H., and R. K. Linsley (1966). Digital simulation in hydrology: Stanford watershed model IV, Tech. Rep. 39, Dep. of Civ. Eng., Stanford, Calif.
13 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
14 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
15 Kimura, Toshimitsu (1961). 저류함수법에 의한 홍수유출추적법(Flood Runoff Routing by Storage Function Method), 일본건설성 토목연구소
16 P.J. Werbos (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis, Harvard University, Cambridge, MA.
17 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
18 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
19 WMO (1975). Intercomparison of conceptual models used in operational hydrological forecasting, Operational Hydrology Report, No. 7, W.M.O, Geneva