Advances in Neural Networks – ISNN 2009

Volume 5553 of the series Lecture Notes in Computer Science pp 49-58

A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimization Technique Coupled with Artificial Neural Network

  • Jiansheng WuAffiliated withDepartment of Mathematics and Computer, Liuzhou Teacher College
  • , Enhong ChenAffiliated withDepartment of Computer, University of Science and Technology of China

* Final gross prices may vary according to local VAT.

Get Access


In this study, we propose a novel nonparametric regression (NR) ensemble rainfall forecasting model integrating generalized particle swarm optimization (PSO) with artificial neural network (ANN). First of all, the PSO algorithm is used to evolve neural network architecture and connection weights. The evolved neural network architecture and connection weights are input into a new neural network.The new neural network is trained using back-propagation (BP) algorithm, generating different individual neural network. Then, the principal component analysis (PCA) technology is adopted to extract ensemble members. Finally, the NR is used for nonlinear ensemble model. Empirical results obtained reveal that the prediction by using the NR ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. For illustration and testing reveal that the NR ensemble model proposed can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.


Nonparametric Regression Neural Network Ensemble Particle Swarm Optimization Rainfall Forecasting