An Artificial Intelligence Prediction Model Based on Principal Component Analysis for Typhoon Tracks

We developed a novel nonlinear artificial intelligence ensemble prediction (NAIEP) model based on multiple neural networks with identical expected output created by using the genetic algorithm (GA) of evolutionary computation. We extracted the main signal feature from the meteorological fields with random noise and eliminated the random disturbance by principal component analysis (PCA). We set up the NAIEP model based on data of typhoons that occurred in the South China Sea from June to September in the period 1980-2010. The predictors were selected by the stepwise regression method and PCA both in the predictors of climatology persistence and Numerical Weather Prediction (NWP) products to predict the typhoon tracks for each month. Under the condition of identical model samples and independent prediction sample cases, we compared the genetic neural network ensemble prediction (GNNEP) model by selecting the predictors with both the method of Stepwise regression with PCA and the climatology and persistance(CLIPER) prediction model. The result showed that the former method was more accurate than the latter, and the average absolute error of the typhoon track from June to September decreased by 7.4%, 4.8%, 12.4%, and 17.0%, respectively. Under the condition of identical primary predictors and sample cases, we compared the prediction accuracies of the new model, the model of Stepwise regression, and the model of GNNEP (using only the method of PCA for the input predictors), and theoretically proved that the new model is more accurate than the other two. In the method which uses forecast information in all the alternative predictors and in the GNNEP model in which the resultant prediction from the ensemble integrates the predictions of the multiple ensemble members, the network structure is determined through the optimizing computation of GA;therefore, the generalization capacity of the ensemble prediction model is improved, leading to better availability and improved weather prediction