Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
권호 표지

Development of Flow Interpolation Model Using Neural Network and its Application in Nakdong River Basin

유량 보간 신경망 모형의 개발 및 낙동강 유역에 적용

  • Son, Ah Long (School of Archi. & Civil Engineering, Kyungpook National Univ.) ;
  • Han, Kun Yeon (School of Archi. & Civil Engineering, Kyungpook National Univ.) ;
  • Kim, Ji Eun (School of Archi. & Civil Engineering, Kyungpook National Univ.)
  • 손아롱 (경북대학교 건설, 토목공학부) ;
  • 한건연 (경북대학교 건설, 토목공학부) ;
  • 김지은 (경북대학교 건설, 토목공학부)
  • Received : 2009.07.01
  • Accepted : 2009.09.22
  • Published : 2009.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this study is to develop a reliable flow forecasting model based on neural network algorithm in order to provide flow rate at stream sections without flow measurement in Nakdong river. Stream flow rate measured at 8-days interval by Nakdong river environment research center, daily upper dam discharge and precipitation data connecting upstream stage gauge were used in this development. Back propagation neural network and multi-layer with hidden layer that exists between input and output layer are used in model learning and constructing, respectively. Model calibration and verification is conducted based on observed data from 3 station in Nakdong river.

Keywords

References

  1. 낙동강물환경연구소, 2005, (오염총량관리를 위한 '05) 낙동강수계 유량측정사업 운영결과
  2. 낙동강물환경연구소, 2006, (오염총량관리를 위한 '06) 낙동강수계 유량측정사업 운영결과
  3. 김성원, 2001, 하천유역에서 홍수유출량의 예측을 위한 신경망기법의 적용, 대한토목학회논문집, 20(6-B), 801-811
  4. 김주환, 1993, 신경회로망을 이용한 하천유출량의 수문학적 예측에 관한 연구, 박사학위 논문, 인하대학교
  5. 윤강훈, 서봉철, 2004, 댐방류량을 이용한 한강 유역 신경망 유출 예측모형에 관한 연구, 한국수자원학회 논문집, 24(1B), 47-53
  6. Anthony, W. M., 1996, Extended Rainfall- Runoff Modelling using Artificial Neural Networks. Hydroinformatics '96, Proceedings of the 2nd International Conference on Hydroinformatics, Zurich, Switzerland, pp. 207-213
  7. Fletcher, D. S. and Goss, E., 1993, Forecasting with neural networks: an application using bankruptcy data, Information & Management, 24, 159-167 https://doi.org/10.1016/0378-7206(93)90064-Z
  8. Kerh, T. and Lee, C. S., 2006, Neural networks forecasting of flood discharge at an unmeasured station using river upstream information, Advances in Engineering Software, 37(8), 533-543 https://doi.org/10.1016/j.advengsoft.2005.11.002
  9. Kwok, T. Y. and Yeung, D. Y., 1997a, Constructive algorithms for structure learning in feedforward neural networks for regression problems, IEEE Transactions on Neural Networks, 8(1), 630-645 https://doi.org/10.1109/72.572102
  10. Kwok, T. Y. and Yeung, D. Y., 1997b, Objective functions for training new hidden units in constructive neural networks, IEEE Transactions on Neural Networks, 8(5), 1131-1148 https://doi.org/10.1109/72.623214
  11. Lachtermacher G. and Fuller J. D., 1995, Backpropagation in time-series forecasting, Journal of Forecasting, 14(4), 381-393 https://doi.org/10.1002/for.3980140405
  12. M. Erkan Turan and M. Ali Yurdusev, 2009, River flow estimation from upstream flow records by artificial intelligence methods, Journal of Hydrology, 369, 71- 77 https://doi.org/10.1016/j.jhydrol.2009.02.004
  13. Minns, A. W. and Hall, M. J., 1996, Artificial neural networks as rainfall-runoff models, Hydrological Sciences Journal, 41(3), 399-417 https://doi.org/10.1080/02626669609491511
  14. Thirumalaiah, K., 1998, River stage forecasting using artificial neural networks, ASCE Journal of Hydrologic Engineering, 3(1), 26-32 https://doi.org/10.1061/(ASCE)1084-0699(1998)3:1(26)
  15. Zhang, B. and Govindaraju, R. S., 2003, Geomorphology-based Artificial Neural Networks (GANNs) for Estimation of Direct Runoff over Watersheds, Journal of Hydrology, 237, 18-34