Forecasting of basin sediment yield based on wavelet-BP neural network
| Autor: | Yao Chuan-an, Li Shixin, Wen Jian, Shao Xiao-hou, Huang Xin |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Sediment yield
Engineering Wavelet neural network Artificial neural network business.industry Wavelet transform Soil science Time sequence Structural basin Machine learning computer.software_genre Backpropagation Physics::Geophysics Wavelet Artificial intelligence business computer Physics::Atmospheric and Oceanic Physics |
| Zdroj: | 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010). |
| DOI: | 10.1109/car.2010.5456767 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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