한국농공학회지 (Magazine of the Korean Society of Agricultural Engineers)

한국농공학회 (The Korean Society of Agricultural Engineers)
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인공지능기법을 이용한 일유출량의 추계학적 비선형해석

A Stochastic Nonlinear Analysis of Daily Runoff Discharge Using Artificial Intelligence Technique

  • 발행 : 1997.06.01
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초록

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

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참고문헌

  1. 국제수문개발계획(IHP) 대표유역 연구조사보고서 건설교통부
  2. 인하대학교 박사학위논문 신경회로망을 이용한 하천유출량의 수문학적 예측에 관한 연구 김주환
  3. 한국수자원학회지 v.29 no.4 신경망이론에 의한 강우예측에 관한 연구 오남선;선우중호
  4. 한국농공학회지 v.36 no.2 신경회로망을 이용한 수도증발산량 예측-백 프로파게이션과 카운터 프라파게이션 알고리즘의 적용 이남호;정하우
  5. Annual of Math. Statistics v.8 no.1 Distribution of the serial correlation coefficients Anderson,R.L.
  6. Introduction to the theory of Neural Computation Hertz,J.;Krogh,A.;Plamer,R.G.
  7. J. of Water Resour. Plng. and Mgmt. v.121 no.6 Neural-Network Models of Rainfall-Runoff Process Jason Smith;Robert,N.Eli
  8. J. of Comp. in Civil Engineering v.8 no.2 Neural networks for river prediction Karunanithi,N.;Grenney,W.J.;Whitley,D.;Bovee,K.
  9. Los Alamos National Laboratory Report, LA-UR-87-2662 Non-Linear Signal Processing using Neural Networks : Prediction and System Modelling Lapedes,A.S.;Farber,R.
  10. M. S. thesis, West Virginia Univ. Streamflow forecasting using a backpropagation neural network Smith,J.
  11. Adaptive Pattern Recognition and Neural Networks Yoh-Han Pao