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

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.