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 03:22 |
| Last Modified: | 12 Sep 2016 02:42 |
| URI: | https://repository.usp.ac.fj/id/eprint/8434 |
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