Magazine of the Korean Society of Agricultural Engineers (한국농공학회지)

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

인공신경망 이론을 이용한 소유역에서의 장기 유출 해석

  • 강문성 (서울대학교 대학원(박사수료)) ;
  • 박승우 (서울대학교 농업생명과학대학)
  • Published : 2001.03.01
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Abstract

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.

Keywords

References

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