A semiparametric regression ensemble model for rainfall forecasting based on RBF neural network
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Semi-parametric smoothing regression model based on GA for financial time series forecasting
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
| Hi-index | 0.00 |
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.