Application of neural network model to Guangxi ensemble precipitation prediction

Author

Mengsong Nong

Author_Institution

Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China

fYear

2012

fDate

6-8 July 2012

Firstpage

454

Lastpage

457

Abstract

Using the method of artificial neural networks and principal component analysis (PCA) to study on a variety of numerical forecast products for the same precipitation forecast. The results showed that the fitting accuracy of the principal component analysis artificial neural network ensemble model is better than each sub-product and the experimental results of the independent samples also shows its better prediction accuracy and stability. The model is a good prospects for business applications.

Keywords

atmospheric precipitation; geophysics computing; neural nets; principal component analysis; weather forecasting; Guangxi ensemble precipitation prediction; PCA; artificial neural networks; fitting accuracy; neural network model; numerical forecast products; precipitation forecast; prediction accuracy; principal component analysis; stability; Analytical models; Artificial neural networks; Predictive models; Principal component analysis; Weather forecasting; ensemble prediction; ne ural network; principal component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2012 10th World Congress on

Conference_Location

Beijing

Print_ISBN

978-1-4673-1397-1

Type

conf

DOI

10.1109/WCICA.2012.6357918

Filename

6357918