A new model of synthetic integration for meteorological forecast based on neural networks and fuzzy logic

Author

Weihong, Wang ; Min, Yao

Author_Institution

Dept. of Comput. Eng., Zhejiang Univ. of Technol., Hangzhou, China

Volume

2

fYear

2002

fDate

26-30 Aug. 2002

Firstpage

1827

Abstract

Combination methods of neural networks and fuzzy logic are briefly surveyed. Then, a novel combination model is presented for synthetic integration of rainfall. The presented model is composed of four network layers: input layer, membership function construction layer, inference layer and defuzzification layer. The combination model is applied to synthetic integration of forecasted rainfall data produced by gradual regression method, periodic analysis plus multi-layer method and model output statistics method. The model is trained by short-term rainfall data of Zhejiang Province from 1980 to 1997. The synthetic integration (forecast) results from 1998 to 2000 show that the presented model can obtain satisfactory forecast performance.

Keywords

fuzzy logic; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); rain; statistical analysis; weather forecasting; Zhejiang Province; defuzzification layer; fuzzy logic; gradual regression method; inference layer; input layer; membership function construction layer; meteorological forecast; model output statistics method; multi-layer method; neural networks; periodic analysis; rainfall; rainfall forecast data; synthetic integration; Demand forecasting; Economic forecasting; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Meteorology; Neural networks; Predictive models; Technology forecasting; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2002 6th International Conference on

Print_ISBN

0-7803-7488-6

Type

conf

DOI

10.1109/ICOSP.2002.1180159

Filename

1180159