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Research Journal of Environmental Sciences
  Year: 2008 | Volume: 2 | Issue: 5 | Page No.: 385-392
DOI: 10.3923/rjes.2008.385.392
 
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Developing of Halil River Rainfall-Runoff Model, Using Conjunction of Wavelet Transform and Artificial Neural Networks

Mohamad Bagher Rahnama and Mojtaba Noury

Abstract:
In present study, stream flow forecasting performance of Halil river basin, located in Kerman South of Iran, had been investigate. Artificial Neural Networks (ANN) is a positive mathematical tool to represent complex relationships in many branches in hydrology. A multi-layer artificial neural networks and a neuro-wavelet hybrid system were used. The proposed conjunction model is based on use of wavelet transform and artificial neural networks. Daily precipitation and runoff data of Halil river basin were used to train ANN's model. Then it was used to forecast the stream flow from the rainfall information. The final result indicates that the conjunction model significantly improves the ability of neural networks to forecast the daily stream flow for Halil river basin. It can be proposed that this model capable to predict the maximum stream flow of the river, which could help to design hydraulic structure and it will be very useful for the management of the dam.
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How to cite this article:

Mohamad Bagher Rahnama and Mojtaba Noury, 2008. Developing of Halil River Rainfall-Runoff Model, Using Conjunction of Wavelet Transform and Artificial Neural Networks. Research Journal of Environmental Sciences, 2: 385-392.

DOI: 10.3923/rjes.2008.385.392

URL: https://scialert.net/abstract/?doi=rjes.2008.385.392

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