A Neural Network Ensemble Prediction Model Based On Nonlinear Kernel Principal Component Analysis For Typhoon Intensity

A new objective prediction model has been developed for predicting typhoon intensitybased on neural network, genetic algorithm, Kernel Principal Component Analysis (KPCA) andusing the ensemble prediction theory of numerical weather prediction, due to the fact thattyphoon intensity is characteristic of nonlinearity and transientness. Typhoon intensity data weretaken from the"Typhoon Almanac"published by the China Meteorological Administration from1980 through 2008.To construct a new neural network ensemble prediction model for typhoon intensity, a back-propagation (BP) network is used as the basic model. The BP network has advantages ofadaptive learning, nonlinear mapping and so forth. But this network is difficult to determinedobjectively and yield"over-fitting". So genetic algorithm is applied for optimize both of theneural network structure and connection weights with its global search characteristicsmeantime 3 genetic operators called selection crossover and mutation are used to exchangeinformation among individuals continuously until the best also the last generation of geneticpopulation in evolution process is reserved which worked as the member of ensemble predictionmodel than compound each forecast result of individuals with the same weight to set up a neuralnetwork ensemble prediction model thereby.Generally, for the treatment of predictors in the practical typhoon intensity prediction, thefactors, that have high individual correlation coefficients with the predictor, are treated usingstepwise regression method to select predictors for modeling, but the predictors, that areeliminated by stepwise regression method and have high prediction information, are discarded.While, if the eliminated factors with a number of prediction information that stepwise regressionselected are all used, too long training time may lead to over-fitting. So KPCA method is appliedfor feature extraction from the eliminated factors that linear regression equation selected, thanchoose few of the KPCA which contain the most prediction information with the factorsstepwise regression selected as input data for the ensemble prediction model. KPCA combinedKernel method and Principal Component Analysis, use PCA method after carried the data frominput space to feature space by nonlinear mapping, at last, change dot-product operation in thefeature space into kernel calculation in the input space, thus be able to extract non-linear relationship in data.According to the above research of model construction and treatment method for predictor,a neural network ensemble prediction model has been established base on KPCA. Take theclimatology and persistence factors as the primary factors to set up 4 typhoon intensity ensembleprediction model with 24 hours forecast aging based on the data of the typhoon intensity with 48hours life history in June July August and September from 1980 to 2008 respectively for test,which the data from 1980 to 1999 as modeling samples and the data from 2000 to 2008 asindependent prediction samples. As 28 to 31 factors selected by correlation coefficient eachmonth, but only 5 to 8 factors reserved after stepwise regression used, so KPCA method isapplied for feature extraction from the eliminated factors that stepwise regression selected, thanchoose few of the KPCA which contain the most prediction information with the factors stepwiseregression selected as input data to establish the neural network ensemble prediction model eachmonth thereby. The statistical results show that the mean absolute error of June July August andSeptember are 4.58 m /s 4.52 m /s 3.13 m /s 4.58 m /s respectively. In order to investigate theforecasting capability of this model, traditional linear regression prediction equation for the sameindependent prediction samples is discussed based on the same data, the corresponding error are4.84 m /s 5.58 m /s 3.68 m /s and 5.14 m /s . The error of ensemble prediction decreased5.25% 19.12% 14.94% and 10.81% than linear regression prediction respectively (the meanrelative error).The results show that the neural network ensemble prediction model based on KPCA ismore accurate than the traditional stepwise regression method. The reason is that, in the newmodel, the predictors that are eliminated, were treated using KPCA, and then their usefulinformation was added into the prediction model. Thus the new model contains more effectiveprediction information that can improve the forecast effect of the ensemble prediction model.Furthermore both of the way to model construction and the treatment technology for predictorhave a good reference significance for the research of prediction modeling in related fields.