BP Neural Network Model Based on Chaos Theory and Application in Ground Water Level Forecasting

Author

Sun, Xiu-ling ; XU, Xiao-chi ; TAN, Yong-ming

Author_Institution

Sch. of Civil Eng., Shandong Univ., Jinan

Volume

3

fYear

2008

fDate

20-22 Dec. 2008

Firstpage

445

Lastpage

448

Abstract

By the main component analysis, and maximum Lyapunov index method, this paper analyses chaotic character of ground water level time series. On this basis, combining the reconstruction phase space of chaos theory with BP neural network to set up a BP neural network model based on chaos theory. This paper forecasts ground water level of the Heihu Spring in Jinan by the model. The result shows that the model has a very good forecast accuracy and value. This method can provide a new way for going deep into forecasting Heihu spring discharge.

Keywords

backpropagation; chaos; forecasting theory; geophysics computing; groundwater; neural nets; time series; BP neural network model; Heihu Spring discharge forecasting; chaos theory; ground water level time series forecasting; main component analysis; maximum Lyapunov index method; phase space reconstruction; Biological neural networks; Chaos; Information analysis; Neural networks; Predictive models; Space technology; Springs; Technology forecasting; Time series analysis; Water resources; chaos neural network; chaotic character; forecasting; ground water level; time series;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3497-8

Type

conf

DOI

10.1109/IITA.2008.91

Filename

4740036