DocumentCode :
2318817
Title :
A novel artificial neural network ensemble model based on K-nn nonparametric estimation for rainfall forecasting
Author :
Nong, Jifu
Author_Institution :
Coll. of Math. & Comput. Sci., Guangxi Univ. for Nat., Nanning, China
fYear :
2010
fDate :
25-27 Aug. 2010
Firstpage :
76
Lastpage :
80
Abstract :
In this paper, we propose a novel artificial neural network ensemble rainfall forecasting model based K-nearest neighbor (K-nn) nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then using different ANNs algorithms and different network architecture generate diverse individual neural network ensemble by training subsets, Thirdly, the partial least square regression is adopted to extract ensemble members. Finally, the K-nn nonparametric regression is used for ensemble model. Empirical results obtained reveal that the prediction by using the nonparametric ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonparametric ensemble model proposed here can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.
Keywords :
artificial intelligence; geophysics computing; least squares approximations; neural nets; parameter estimation; pattern classification; rain; regression analysis; weather forecasting; ANN algorithms; artificial neural network ensemble model; bagging technology; k-nearest neighbor nonparametric estimation; k-nn nonparametric estimation; network architecture; partial least square regression; rainfall forecasting model; Artificial neural networks; Biological system modeling; Data models; Estimation; Forecasting; Predictive models; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computational Intelligence (IWACI), 2010 Third International Workshop on
Conference_Location :
Suzhou, Jiangsu
Print_ISBN :
978-1-4244-6334-3
Type :
conf
DOI :
10.1109/IWACI.2010.5585228
Filename :
5585228
Link To Document :
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