Forecasting of basin sediment yield based on wavelet-BP neural network

  • Authors:

  • Li Shixin;Yao Chuanan;Wen Jian;Huang Xin;Shao Xiaohou

  • Affiliations:

  • College of Modern Agncultural Engineering, Hohai University Nanjing, China and College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, China;College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, China;College of Information Management Science, Henan Agricultural University, Zhengzhou, China;Institute of Environment and Municipal Engineering, North China University of Water Conservancy and Electric Power, Zhengzhou, China;College of Modern Agncultural Engineering, Hohai University Nanjing, China

  • Venue:
  • CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
  • Year:
  • 2010

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Abstract

Based on the advantages of both wavelet analysis and artificial neural network, the wavelet neural network (WNN) model is established through coupling wavelet transform with BP neural network for forecasting the basin sediment yield. The time sequence of the annual sediment yield is decomposed and reconstructed into the low-frequency and high-frequency components by wavelet transform; then these components are predicted by optimized BP neural network respectively. Finally, the sum of the predicting values is the forecasting result of the sediment yield. The result shows that the hybrid model, compared with the traditional BP (TB) model, has high accuracy in the simulation and test of basin sediment yield, which can provide a scientific basis for ecological environment protection and water resource management in a basin.