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Article Information:
A Wavelet Neural Network Hybrid Model for Monthly Ammonia Forecasting in River Water
Yi Wang, Yuanyuan Wang, Liang Guo, Ying Zhao, Zhichao Zhang and Peng Wang
Corresponding Author: Peng Wang
Submitted: November 20,2012
Accepted: December 15, 2012
Published: June 10, 2013 |
Abstract:
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Forecasting water quality is always an effective approach for water environmental management. This study presents a combined Wavelet transform (WA) and Artificial Neural Network (ANN) model for monthly ammonia nitrogen series prediction in river water. The WA decomposed original time series into different subseries, in which the most significant one was chosen as the training data instead of the original series. Compared to the traditional ANN, the WA-ANN models were found more accurate and reliable. The results of the study indicate that WA could remove the noise of the original datasets and the WA-ANN could help environment decision-maker manage water quality more effective.
Key words: Artificial neural network, environment management, Harbin region, time series, water quality forecasting, wavelet analysis ,
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Cite this Reference:
Yi Wang, Yuanyuan Wang, Liang Guo, Ying Zhao, Zhichao Zhang and Peng Wang, . A Wavelet Neural Network Hybrid Model for Monthly Ammonia Forecasting in River Water. Research Journal of Applied Sciences, Engineering and Technology, (02): 345-348.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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