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yca(金币+5): 2010-06-17 09:55:00
xiaogounihao(金币+1):谢谢 2010-06-17 15:23:44
Accession number: 20102312991925
Title: Predication of sediment yield using wavelet - Neural networks
Authors: Yao, Chuan-An1 ; Yu, Yong-Chang1
Author affiliation: 1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China
Corresponding author: Yao, C.-A. (ycagl@163.com)
Source title: 2010 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010
Abbreviated source title: Int. Conf. Meas. Technol. Mechatronics Autom., ICMTMA
Volume: 2
Monograph title: 2010 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010
Issue date: 2010
Publication year: 2010
Pages: 911-914
Article number: 5459971
Language: English
ISBN-13: 9780769539621
Document type: Conference article (CA)
Conference name: International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010
Conference date: March 13, 2010 - March 14, 2010
Conference location: Changsha, China
Conference code: 80501
Sponsor: Changsha University of Science and Technology; Hunan University of Science and Technology; IEEE Instrumentation and Measurement Society; City University of Hong Kong
Publisher: IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
Abstract: Accurate prediction of watershed sediment yield is important for ecological environment and water resources engineering. Based on the advantages of wavelet analysis and neural networks, a new hybrid model of wavelet transform and BP neural network (wavelet-neural network model, WANN) for predicting the sediment yield, has been suggested in this paper. The established WANN model is applied to make quantitative prediction of annual sediment yield for Hepinggou's small watershed, located in the southwest of China's Henan Province. First, the annual sediment yield time series is decomposed and reconstructed into the low-frequency component and the high-frequency components at one-scale level by db2 wavelet, and then both are forecasted respectively with BP neural networks. Finally, the sum of two parts is the predicting result of the annual sediment yield. Results show that the suggested model can improve the forecasting accuracy; it can also be successfully applied to prediction of hydrological time series. © 2010 IEEE.
Number of references: 7
Main heading: Neural networks
Controlled terms: Forecasting - Landforms - Mechatronics - Sedimentology - Speech recognition - Time series - Water resources - Watersheds - Wavelet analysis - Wavelet transforms
Uncontrolled terms: Accurate prediction - BP neural networks - Ecological environments - Forecasting accuracy - Henan Province - High frequency components - Hybrid model - Hydrological time-series - Low-frequency components - Neural network model - Prediction - Quantitative prediction - Sediment yields - Small watersheds
Classification code: 723.5 Computer Applications - 731.7 Mechatronix - 751.5 Speech - 922.2 Mathematical Statistics - 912.2 Management - 921 Mathematics - 921.3 Mathematical Transformations - 913.4 Manufacturing - 723.4 Artificial Intelligence - 723.2 Data Processing and Image Processing - 444 Water Resources - 444.1 Surface Water - 461.1 Biomedical Engineering - 481.1 Geology - 608 Mechanical Engineering, General - 703.2.1 Electric Filter Analysis
DOI: 10.1109/ICMTMA.2010.794
Database: Compendex
Compilation and indexing terms, © 2009 Elsevier Inc. |
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