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Cooperative neuro - evolution of Elman recurrent networks for tropical cyclone wind - intensity prediction in the South Pacific region

Chandra, Rohitash and Dayal, Kavina (2015) Cooperative neuro - evolution of Elman recurrent networks for tropical cyclone wind - intensity prediction in the South Pacific region. [Conference Proceedings]

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    Abstract

    Climate change issues are continuously on the rise and the need to build models and software systems for management of natural disasters such as cyclones is increasing. Cyclone wind-intensity prediction looks into efficient models to forecast the wind-intensification in tropical cyclones which can be used as a means of taking precautionary measures. If the wind-intensity is determined with high precision a few hours prior, evacuation and further precautionary measures can take place. Neural networks have become popular as efficient tools for forecasting. Recent work in neuro-evolution of Elman recurrent neural network showed promising performance for benchmark problems. This paper employs Cooperative Coevolution method for training Elman recurrent neural networks for Cyclone wind- intensity prediction in the South Pacific region. The results show very promising performance in terms of prediction using different parameters in time series data reconstruction.

    Item Type: Conference Proceedings
    Additional Information: DOI: 10.1109/CEC.2015.7257103
    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 15:22
    Last Modified: 12 Sep 2016 14:42
    URI: http://repository.usp.ac.fj/id/eprint/8434

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