Application of cooperative neuro-evolution of Elman recurrent networks for a two-dimensional cyclone track prediction for the south pacific region

This paper presents a two-dimensional time series prediction approach for cyclone track prediction using cooperative neuro-evolution of Elman recurrent networks in the South Pacific region. The latitude and longitude of tracks of cyclone lifetime is taken into consideration for past three decades to build a robust forecasting system. The proposed method performs one step ahead prediction of the cyclone position which is essentially a two-dimensional time series prediction problem. The results show that the Elman recurrent network is able to achieve very good accuracy in terms of prediction of the tracks which can be used as means of taking precautionary measures.