A Novel Artificial Neural Network Ensemble Model Based on K-Nearest Neighbor Nonparametric Estimation of Regression Function and Its Application for Rainfall Forecasting
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.
Jiansheng Wu
Department of Mathematics and Computer Liuzhou Teachers College Guangxi, Liuzhou, China
国际会议
三亚
英文
1094-1098
2009-04-24（万方平台首次上网日期，不代表论文的发表时间）