Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Study on Water Stage Prediction Using Hybrid Model of Artificial Neural Network and Genetic Algorithm

인공신경망과 유전자알고리즘의 결합모형을 이용한 수위예측에 관한 연구

  • 여운기 (영남대학교 건설시스템공학과) ;
  • 서영민 (영남대학교 건설시스템공학과) ;
  • 이승윤 (한국수자원공사, K-water 수자원연구원) ;
  • 지홍기 (영남대학교 건설시스템공학과)
  • Received : 2010.07.13
  • Accepted : 2010.08.04
  • Published : 2010.08.31
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Abstract

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

강우-유출 관계는 유역의 수많은 시 공간적 변수들에 의해 영향을 받기 때문에 매우 복잡하여 예측하기 힘든 요소이다. 과거에는 추계학적 예측모형이나 확정론적 예측모형 혹은 경험적 모형 등을 사용하여 유출량을 예측하였으나 최근에는 인공신경망과 퍼지모형 그리고 유전자 알고리즘과 같은 인공지능기반의 모형들이 많이 사용되고 있다. 하지만 유출량을 예측하고자 할 때 학습자료 및 검정자료로써 사용되는 유출량은 수위-유량 관계곡선식으로부터 구하는 경우가 대부분으로 이는 이렇게 유도된 유출량의 경우 오차가 크기 때문에 그 신뢰성에 문제가 있을 것으로 판단된다. 따라서 본 논문에서는 선행우량 및 수위자료로부터 단시간 수위예측에 관해 연구하였으며, 이를 위해 오류역전파알고리즘을 이용한 신경망모형과 인공신경망의 가중치를 유전자 알고리즘에 의해 최적화시킨 모형 그리고 최적화된 상태에서 다시 학습을 진행하는 세 가지 모형에 대하여 적용한 결과 유전자 알고리즘을 사용하여 신경망을 학습시킨 모형이 다른 모형들에 비해 우수한 결과를 보여주고 있으며 예측시간이 길어지더라도 예측력이 크게 떨어지지 않았다. 또한 입력자료로 강우와 수위를 사용한 모형보다는 수위를 사용한 모형에서 조금 더 우수한 결과를 보여주었다.

Keywords

References

  1. 김성원(2000). “하천유역에서 홍수유출량의 예측을 위한 신경망기법의 적용.” 대한토목학회논문집, 대한토목학회, 제20권, 제6-B호, pp. 801-811.
  2. 박성천, 오창열, 김동렬, 진영훈(2006). “인공신경망 이론을 이용한 홍수유출 예측시스템 개발 -GUI_FFS 개발 및 적용-.” 대한토목학회논문집, 대한토목학회, 제26권, 제2B호, pp. 145-152.
  3. 박성천, 이용, 진영훈, 오창열(2005). “신경망 이론을 이용한 상․하류지점간의 수위 예측.” 한국수처리학회지, 한국수처리학회, 제13권, 제3호, pp. 45-54.
  4. 오남선, 선우중호(1996). “신경망이론에 의한 강우예측에 관한 연구.” 한국수자원학회지, 한국수자원학회, 제29권, 제4호, pp. 109-118.
  5. 이순탁, 김성원(1997). “홍수유출량 예측을 위한 인공신경망모델의 적용에 관한 연구.” 환경연구, 영남대학교 환경문제연구소, 제16권, 제2호, pp. 47-62.
  6. 황현경, 김경호(2008). “신경망 모형을 이용한 무심천 수위 예측.” 건설기술연구소논문집, 충북대학교, 제27권, 제1호, pp. 43-56.
  7. Bazartseren, B., Hildebrandt, G., and Holz, K.P. (2003). “Short-term water level prediction using neural networks and neuro-fuzzy approach.” Neurocomputing, Vol. 55, No. 3-4, pp. 439-450. https://doi.org/10.1016/S0925-2312(03)00388-6
  8. Chau, K.W. (2006). “Particle swarm optimization training algorithm for ANNs in stage prediction of shing mun river.” Journal of Hydrology, Vol. 329, No. 3-4, pp. 363-367. https://doi.org/10.1016/j.jhydrol.2006.02.025
  9. De Jong, K.A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Ph.D dissertation, The University of Michigan, Ann Arbor, Michigan.
  10. French, M.N., Krajewski, W.F., and Cuykendall, R.R. (1992). “Rainfall forecasting in space and time using a neural network.” Journal of Hydrology, Vol. 137, No. 1-4, pp. 1-31. https://doi.org/10.1016/0022-1694(92)90046-X
  11. Goldberg, D.E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.
  12. Holland, J.H. (1975). Adaptation in natural and artificial systems. University of Michigan Press.
  13. Janikow, C.Z., and Michalewicz, Z. (1991). “An experimental comparison of binary and floating point representations in genetic algorithms.” Proceedings 4th International Conference on Genetic Algorithms, California. pp. 31-36.
  14. Kuligowski, R.J., and Barros, A.P. (1998). “Experiments in short-term precipitation forecasting using artificial neural networks.” Monthly Weather Review, Vol. 126, No. 2, pp. 470-482. https://doi.org/10.1175/1520-0493(1998)126<0470:EISTPF>2.0.CO;2
  15. Maniezzo, V. (1994). “Genetic evaluation of the topology and weight distribution of neural network.” IEEE Transaction of Neural Network, Vol. 5, No. 1, pp. 39-53. https://doi.org/10.1109/72.265959
  16. McCulloch, W.S., and Pitts, W. (1943). “A logical calculus of the ideas immanent in nervous activity.” Bulletin of Mathematical Biophysics, Vol. 5, No. 4, pp. 115-133. https://doi.org/10.1007/BF02478259
  17. Michalewicz, Z. (1996). Genetic algorithms + data structures=evolution programs (3rd ed.). Springer-Verlag, Berlin Heidelberg.
  18. Montana, D.J., and Davis, L. (1988). “Training feedforward neural networks using genetic algorithms.” Proceedings 11th International Joint Conference on Artificial Intelligence, San Mateo, California, Vol. 1, pp. 762-767.
  19. Napolitano, G., See, L., Calvo, B., Savi, F., and Heppenstall, A. (2009). “A conceptual and neural network model for real-time flood forecasting of the Tiber river in Rome.” Physics and Chemistry of the Earth, Vol. 35, No. 3-5, pp. 187-194.
  20. Nasseri, M., Asghari, K., and Abedini, M.J. (2008). “Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network.” Expert Systems with Applications, Vol. 35, No. 3, pp. 1415-1421. https://doi.org/10.1016/j.eswa.2007.08.033
  21. Pham, D.T., and Jin, G. (1995). “Genetic algorithm using gradient-like reproduction operator.” Electronics Letters, Vol. 31, No. 18, pp. 1558-1559. https://doi.org/10.1049/el:19951092
  22. Pham, D.T., and Jin, G. (1996). “A hybrid genetic algorithm.” Proceedings of 3rd World Congress on Expert Systems, Seoul, Korea, Vol. 2, pp. 748-757.
  23. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). “Learning representations by back-propagating errors.” Nature, Vol. 323, pp. 533-536. https://doi.org/10.1038/323533a0
  24. Sedki, A., Ouazar, D., and Mazoudi, E.E. (2009). “Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting.” Expert Systems with Applications, Vol. 36, No. 3, pp. 4523-4527. https://doi.org/10.1016/j.eswa.2008.05.024
  25. Srinivasulu, S., and Jain, A. (2006). “A comparative analysis of training methods for artificial neural network rainfall-runoff models.” Applied Soft Computing, Vol. 6, No. 3, pp. 295-306. https://doi.org/10.1016/j.asoc.2005.02.002
  26. Thirumalaiah, K., and Deo, M.C. (1998). “Real-time flood forecasting using neural networks.” Computer-Aided Civil and Infrastructure Engineering, Vol. 13, No. 2, pp. 101-111. https://doi.org/10.1111/0885-9507.00090
  27. Wilson, G., and Khondker, M.H. (2000). “Data selection for a flood forecasting neural network.” Proceedings of the 4th International Conference on Hydroinformatics 2000, Iowa, USA.

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