Surface water quality prediction using ARMARBF model

Author

Zhang, Juan ; Zhu, Changjun

Author_Institution

Coll. of Urban Constr., Hebei Univ. of Eng., Handan, China

Volume

3

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

29

Lastpage

32

Abstract

In view of the defect that the gray method can only predict the tendency approximately and artificial neural network can not predict the future tendency really, a new organic gray neural network model was proposed by the advantages of ARMA and rbf neural network. The neural network was trained to get the optimal structure of neural network. According to the dynamic law of one river water quality in some region, the water quality was predicted in ARMA-RBF model. The results show that the model had highly fitting and predicting precision advantages than other model had.

Keywords

autoregressive moving average processes; environmental science computing; learning (artificial intelligence); radial basis function networks; rivers; water quality; ARMA-RBF Model; neural network training; organic gray neural network model; river water quality; surface water quality prediction; Artificial neural networks; Atmosphere; Equations; Intelligent transportation systems; Neural networks; Predictive models; Statistical analysis; Stochastic processes; Time series analysis; Yttrium; ARMA; RBF neural network; water Quality;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics and Intelligent Transportation System (PEITS), 2009 2nd International Conference on

Conference_Location

Shenzhen

Print_ISBN

978-1-4244-4544-8

Type

conf

DOI

10.1109/PEITS.2009.5406860

Filename

5406860