NDLI: A Novel Artificial Neural Network Ensemble Model Based on K--Nearest Neighbor Nonparametric Estimation of Regression Function and Its Application for Rainfall Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Jiansheng Wu
Copyright Year	2009
Abstract	In this paper, a novel artificial neural network ensemble rainfall forecasting model is proposed for rainfall forecasting based on K--nearest neighbor nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then different ANN 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 K--nn nonparametric regression 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.
Starting Page	44
Ending Page	48
File Size	411910
Page Count	5
File Format	PDF
ISBN	9780769536057
DOI	10.1109/CSO.2009.307
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	artificial neural network Statistical analysis Weather forecasting Artificial neural networks Predictive models ensemble Wind forecasting nonparametric Neural networks Disaster management K-nearest neighbor Computer networks Mathematical model Meteorology
Content Type	Text
Resource Type	Article

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