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Coevolutionary recurrent neural networks for prediction of rapid intensification in wind intensity of tropical cyclones in the South Pacific region

Chandra, Rohitash and Dayal, Kavina (2015) Coevolutionary recurrent neural networks for prediction of rapid intensification in wind intensity of tropical cyclones in the South Pacific region. In: Neural Information Processing. Lecture Notes in Computer Science, 9491 . Springer International Publishing, Switzerland, pp. 43-52. ISBN 978-3-319-26554-4

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    Abstract

    Rapid intensification in tropical cyclones occur where there is dramatic change in wind-intensity over a short period of time. Recurrent neural networks trained using cooperative coevolution have shown very promising performance for time series prediction problems. In this paper, they are used for prediction of rapid intensification in tropical cyclones in the South Pacific region. An analysis of the tropical cyclones and the occurrences of rapid intensification cases is assessed and then data is gathered for recurrent neural network for rapid intensification predication. The results are promising that motivate the implementation of the system in future using cloud computing infrastructure linked with mobile applications to create awareness.

    Item Type: Book Chapter
    Additional Information: Proceedings of the 22nd International Conference on Neural Information Processing. Paper presented at the International Conference on Neural Information Processing, November 9-12 2015, Istanbul,Turkey
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Rohitash Chandra
    Date Deposited: 10 Mar 2016 16:02
    Last Modified: 10 Jun 2016 09:48
    URI: http://repository.usp.ac.fj/id/eprint/8430
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