한국농공학회지 (Magazine of the Korean Society of Agricultural Engineers)

한국농공학회 (The Korean Society of Agricultural Engineers)
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Forecasting Long-Term Steamflow from a Small Waterhed Using Artificial Neural Network

  • 강문성 (서울대학교 대학원(박사수료)) ;
  • 박승우 (서울대학교 농업생명과학대학)
  • 발행 : 2001.03.01
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초록

An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.

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참고문헌

  1. 인하대학교 박사학위논문 신경회로망을 이용한 하천유출량의 수문학적 예측에 관한 연구 김주환
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