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

  • Jiansheng Wu
  • Enhong Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5553)

Abstract

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.

Keywords

Nonparametric Regression Neural Network Ensemble Particle Swarm Optimization Rainfall Forecasting 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jiansheng Wu
    • 1
  • Enhong Chen
    • 2
  1. 1.Department of Mathematics and ComputerLiuzhou Teacher CollegeGuangxiChina
  2. 2.Department of ComputerUniversity of Science and Technology of ChinaHefeiChina

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