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     Research Journal of Applied Sciences, Engineering and Technology


Analysis and Prediction of Annual Runoff for Fen River Basin

1Zhang Kai, 2Yang Yonggang, 2Qin Zuodong, 2Li Hongjian and 2Meng Zhilong
1School of Soil and Water Conservation, Beijing Forestry University, Beijing, 100083, China
2Institute of Loess Plateau, Shanxi University, Taiyuan, Shanxi 030006, China
Research Journal of Applied Sciences, Engineering and Technology  2013  5:872-877
http://dx.doi.org/10.19026/rjaset.6.4134  |  © The Author(s) 2013
Received: October 22, 2012  |  Accepted: December 14, 2012  |  Published: June 25, 2013

Abstract

The runoff characteristics of Fen river basin was analyzed first. According to the runoff data of 1960-2000, autocorrelation analysis method was used to determine model input variables and then radial basis function artificial neural network was used to recognize the relationship between previous annual runoff and later annual runoff. The developed prediction model was used to predict the annual runoff of 2001 to 2015 of Fen river basin. The result shows that: the predicted annual runoff was similar to the observed annual runoff, which means high prediction accuracy; the developed radial basis function artificial neural network model can be used for annual runoff prediction of Fen river basin. The research results are of great importance for water resource planning and management.

Keywords:

Annual runoff prediction, fen river basin, radial basis function artificial neural network,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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