A prediction scheme with genetic neural network an

Authors: Ying Huang Long Jin

Publish Date: 2013/05/14

Volume: 121, Issue: 3-4, Pages: 143-152

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Abstract

A western North Pacific tropical cyclone TC intensity prediction scheme has been developed based on climatology and persistence CLIPER factors as potential predictors and using genetic neural network GNN model TC samples during June–October spanning 2001–2010 are used for model development The GNN model input is constructed from potential predictors by employing both a stepwise regression method and an Isometric Mapping Isomap algorithm The Isomap algorithm is capable of finding meaningful lowdimensional architectures hidden in their nonlinear highdimensional data space and separating the underlying factors In this scheme the new developed model which is termed the GNNIsomap model is used for monthly TC intensity prediction at 24 and 48h lead times Using identical modeling samples and independent samples predictions of the GNNIsomap model are compared with the widely used CLIPER method By adopting different numbers of nearest neighbors results of sensitivity experiments show that the mean absolute prediction errors of the independent samples using GNNIsomap model at 24 and 48h forecasts are smaller than those using CLIPER method Positive skills are obtained as compared to the CLIPER method with being above 12  at 24 h and above 14  at 48 h Analyses of the new scheme suggest that the useful linear and nonlinear prediction information of the full pool of potential predictors is excavated in terms of the stepwise regression method and the Isomap algorithm Moreover the GNN is built by integrating multiple individual neural networks with the same expected output and network architecture is optimized by an evolutionary genetic algorithm so the generalization capacity of the GNNIsomap model is significantly enhanced indicating a potentially better operational weather prediction

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