Journal of Korean Society of Water and Wastewater (상하수도학회지)

The Korean Society of Water and Wastewater (대한상하수도학회)
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Forecasting water level of river using Neuro-Genetic algorithm

하천 수위예보를 위한 신경망-유전자알고리즘 결합모형의 실무적 적용성 검토

  • Published : 2012.08.15
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Abstract

As a national river remediation project has been completed, this study has a special interest on the capabilities to predict water levels at various points of the Geum River. To be endowed with intelligent forecasting capabilities, the author formulate the neuro-genetic algorithm associated with the short-term water level prediction model. The results show that neuro-genetic algorithm has considerable potentials to be practically used for water level forecasting, revealing that (1) model optimization can be obtained easily and systematically, and (2) validity in predicting one- or two-day ahead water levels can be fully proved at various points.

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References

  1. 과학기술부 (2004) 실시간 물 관리 운영시스템 구축 기술 개발
  2. 국가 수자원관리종합정보 시스템 웹사이트
  3. 박성천, 김용구, 정천리, 진영훈 (2009) 홍수 예,경보 시스템 개선을 위한 인공신경망 이론의 적용, 한국수자원학회 2009년도 학술발표회, pp. 1271-1265
  4. 황현경, 김경호 (2008) 신경망 모형을 이용한 무심천 수위 예측, 건설기술연구소 논문집, 27(1), pp. 43-56
  5. Alvisi, S., Mascellani, G., Franchini, M. and Bardossy, A. (2006) Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrology and Earth System Sciences, 10, pp. 1-17 https://doi.org/10.5194/hess-10-1-2006
  6. Dibike, Y.B. and Solomatine, D.P. (2001) River Flow Forecasting Using Artificial Neural Networks, Physics and Chemistry of the Earth, Part B, 26(1), pp. 1-7 https://doi.org/10.1016/S1464-1909(01)85005-X
  7. Filho, A.J.P. and Santos, C.C. (2006) Modeling a densely urbanized watershed with an artificial neural network, weather radar and telemetric data. Journal of Hydrology, 317, pp. 31-48. https://doi.org/10.1016/j.jhydrol.2005.05.007
  8. Goldbe rg, D.E. (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley
  9. Haykin , S. (1994) Neural Networks: A Comprehensive Foundation Macmillan Colege Purblishing Company, NewYork
  10. Holland , J.H. (1975) Adaptation in natural and artificial systems. University of Michigan Press.
  11. Imrie, C .E., durucan, S. and Korre, A. (2000) River flow prediction using artificial neural networks: generalisation beyond the calibration range, Journal of Hydrology, 233, pp.138-153 https://doi.org/10.1016/S0022-1694(00)00228-6
  12. Maier, H. , Jain A., Dandy, G. and Sudheer, K. (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions Environmental Modelling and Software, 25(8), pp. 891-909
  13. Napolita no, 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, 35(3-5), pp. 187-194
  14. Rumelh a rt, D.E. and Mcclelland, J.L. (1986) Parallel distributed processing: explorations in the microstructure of cognition. Volume 2. Psychological and Biological Models, MIT Press, Cambridge, MA, USA
  15. Shin, H. G. and Park, H. (1999) An Optimal Design of Water Distribution Networks with Hydraulic Connectivity and the Genetic Algorithm, ASCE 26th annual Water Resources Planning and Management Conference, Arizona, USA, Session 9-D, pp. 1-10
  16. Solomati ne, D.P. and Torres, L.A. (1996) Neural network approximation of a hydrodynamic model in optimizing reservoir operation, In: Proceedings of the second international conference on hydroinformatics, Zurich, pp. 201-206