Long-term prediction for autumn flood season in Danjiangkou Reservoir basin based on OSR-BP neural network

Author

Liu, Yong ; Chen, Yuanfang ; Hu, Jian ; Huang, Qin ; Wang, Yintang

Author_Institution

State Key Lab. of Hydrol.-Water Resources & Hydraulic Eng., Hohai Univ., Nan Jing, China

Volume

4

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

1717

Lastpage

1720

Abstract

A new method, called OSR-BP neural network, for long-term runoff prediction is put forward in this thesis. In order to eliminate input multi-collinearity and phenomenon of overfitting of the neural network, optimal subset regression (OSR) and BackPropagation(BP) neural network is coupled to an integrated, meanwhile, the training and testing error is comprehensively considered to determine the best condition of stop training. On this basis, runoff in September and October in Danjiangkou Reservoir, is simulated from 1956 to 2000, and is predicted from 2001 to 2008 by using of OSR-BP neural network. The result shows that the stability of model is favorable and accuracy is satisfactory whether simulation or prediction, especially for forecasting the characteristics of drought and flood year.

Keywords

backpropagation; floods; geographic information systems; neural nets; regression analysis; reservoirs; Danjiangkou reservoir basin; OSR-BP neural network; autumn flood prediction; drought forecasting; flood forecasting; geographic information system; optimal subset regression neural network; testing error; Accuracy; Artificial neural networks; Correlation; Forecasting; Predictive models; Training; Water resources; Danjiangkou Reservoir; OSR-BP Neural Network; autumn flood season; long-term runoff prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5584555

Filename

5584555