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A prediction scheme with genetic neural network and Isomap algorithm for tropical cyclone intensity change over western North Pacific

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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 low-dimensional architectures hidden in their nonlinear high-dimensional data space and separating the underlying factors. In this scheme, the new developed model, which is termed the GNN-Isomap model, is used for monthly TC intensity prediction at 24- and 48-h lead times. Using identical modeling samples and independent samples, predictions of the GNN-Isomap 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 GNN-Isomap model at 24- and 48-h 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 GNN-Isomap model is significantly enhanced, indicating a potentially better operational weather prediction.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant 41065002 and Grant 61203301) and the Natural Science Foundation of Guangxi (Grant 2011GXNSFE018006).

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Authors and Affiliations

  1. Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning, China

    Ying Huang

  2. Guangxi Climate Center, Nanning, China

    Long Jin

Authors
  1. Ying Huang
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  2. Long Jin
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Corresponding author

Correspondence to Long Jin.

Additional information

Responsible editor: B. Ahrens.

Appendix

Appendix

1.1 Key acronyms and abbreviations

ANN:

Artificial neural network

ANNs:

Artificial neural networks

BPNN:

Back-propagation neural network

BPNNs:

Back-propagation neural networks

CLIPER:

Climatology and persistence

GA:

Genetic algorithm

GNN:

Genetic neural network

GNN-Isomap:

GNN with employing both stepwise regression method and Isomap algorithm for constructing model input

Isomap:

Isometric mapping

MDS:

Multidimensional scaling

TC:

Tropical cyclone

TCs:

Tropical cyclones

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Huang, Y., Jin, L. A prediction scheme with genetic neural network and Isomap algorithm for tropical cyclone intensity change over western North Pacific. Meteorol Atmos Phys 121, 143–152 (2013). https://doi.org/10.1007/s00703-013-0263-7

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  • DOI: https://doi.org/10.1007/s00703-013-0263-7

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