Water-Bloom Medium-Term Prediction Based on Gray-BP Neural Network Method

Author

Zhu, Shiping ; Liu, Zaiwen ; Wang, Xiaoyi ; Dai, Jun

Author_Institution

Sch. of Comput. & Inf. Eng., Beijing Technol. & Bus. Univ., Beijing, China

fYear

2009

fDate

12-14 Dec. 2009

Firstpage

673

Lastpage

676

Abstract

On the basis of studying the mechanism of water bloom, one kind of gray-BP artificial neural network forecasting method is proposed in the paper. The gray theory was used to obtain preliminary forecast of the occurrence trend of water bloom, combined with neural network to implement error compensation for the forecast result. Compared with BP, this method can predict chlorophyll change trend more accurately, and significantly improve the prediction accuracy with the prolongation of prediction period. It provides an effective new method for water bloom medium-term prediction.

Keywords

backpropagation; error compensation; forecasting theory; neural nets; water; chlorophyll change trend; gray BP neural network method; gray theory; implement error compensation; neural network forecasting method; occurrence trend water bloom; prolongation prediction period; water bloom medium term prediction; Accuracy; Artificial neural networks; Biological system modeling; Computer networks; Error compensation; Lakes; Neural networks; Paper technology; Predictive models; Radial basis function networks; error compensation; gray-BP neural network; water bloom medium-term prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Dependable, Autonomic and Secure Computing, 2009. DASC '09. Eighth IEEE International Conference on

Conference_Location

Chengdu

Print_ISBN

978-0-7695-3929-4

Electronic_ISBN

978-1-4244-5421-1

Type

conf

DOI

10.1109/DASC.2009.14

Filename

5380623