Browse > Article
http://dx.doi.org/10.12652/Ksce.2011.31.5B.405

The Application of Adaptive Network-based Fuzzy Inference System (ANFIS) for Modeling the Hourly Runoff in the Gapcheon Watershed  

Kim, Ho Jun (현대건설(주) 토목환경기술개발실)
Chung, Gunhui (한국건설기술연구원 수자원.환경본부)
Lee, Do-Hun (경희대학교 토목공학과)
Lee, Eun Tae (경희대학교 토목공학과)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.31, no.5B, 2011 , pp. 405-414 More about this Journal
Abstract
The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.
Keywords
neuro-fuzzy; ANFIS; rainfall-runoff; membership function;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 고영준, 박진형, 박성천, 이관수(2000) ANFIS를 이용한 유출량 예측, 대한토목학회 2000년도 학술발표회 논문집, 대한토목학회, pp. 517-520.
2 김원규, 김병식, 김형수, 서병하(2001) 뉴로-퍼지를 이용한 홍수량예측에 관한 연구, 대한토목학회 2001년도 학술발표회 논문집, 대한토목학회, pp. 1-4.
3 나창진, 김형수, 김중훈, 윤용남(2002) 강수예측을 위한 퍼지시계열과 뉴로-퍼지 시스템의 적용, 한국수자원학회 2002년도 학술발표회 논문집, 한국수자원학회, pp. 245-250.
4 송희석, 김재경(2009) ANFIS에서 생성된 규칙의 해석용이성 평가, 지능정보연구, 제15권, 제4호, pp. 123-140.
5 안상진, 전계원, 함창학, 한양수(2001) ANFIS를 이용한 용존산소의 예측. 대한토목학회 2001년도 학술발표회 논문집, 대한토목학회, pp. 1-4.
6 이재응, 최장원(2008) Neuro-Fuzzy 추론기법을 이용한 홍수 예.경보, 한국수자원학회논문집, 한국수자원학회, 제41권, 제3호, pp. 341-351.
7 이정규, 김형준(2004) 퍼지-신경망을 이용한 첨두유량 예측에 관한 연구, 대한토목학회논문집, 대한토목학회, 제24권, 제3B호, pp. 209-219.
8 Aqil, M., Kita, I., Yano, A., and Nishiyama, S. (2007) A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. Journal of Hydrology, Vol. 337, pp. 22-34.   DOI   ScienceOn
9 Chang, F.J. and Chen, Y.C. (2001) A counterpropagation fuzzyneural network modeling approach to ral time streamflow prediction, Journal of Hydrology, Vol. 245, pp. 153-164.   DOI
10 Jang, J.-S.R. (1993) ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685.   DOI   ScienceOn
11 Nayak, P.C., Sudheer, K.P., Ragan, D.M., and Ramasastri, K.S. (2004) A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, Vol. 291, pp. 52-66.   DOI
12 Takagi, T. and Sugeno, M. (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, pp. 116-132.
13 Talei, A., Chua, L.H.C., and Wong, T.S.W. (2010) Evaluation of rainfall and discharge inputs used by adaptive network-based fuzzy inference systems (ANFIS) in rainfall-runoff modeling. Journal of Hydrology, Vol. 391, pp. 248-262.   DOI