A novel Neural Network Ensemble model based on sample reconstruction and Projection Pursuit for rainfall forecasting

First of all, Learning matrix of Neural Network get by Projection Pursuit and Particle Swarm Optimization algorithm which Particle Swarm Optimization algorithm optimize projection index from high dimensionality to a lower dimensional subspace, and then many individual neural networks are generated by Samples Reconstruction based on negative correlation learning method. Secondly, the result of ensemble generate by Projection Pursuit Regression based on Particle Swarm Optimization algorithm. Finally, the forecasting model be established by Neural Network Ensemble with Specimen Reconstruct based on Projection Pursuit and Particle Swarm Optimization. The method be used as an alternative forecasting tool for a Meteorological application in the monthly precipitation forecasting of the Guangxi region. The results show that the method can effectively improve the generalization ability of the system in achieving greater forecasting accuracy and improving prediction quality further.