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

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

Identification of minimal timespan problem for recurrent neural networks with application to cyclone wind - intensity prediction

Time series prediction relies on past data points to make robust predictions. The span of past data points is important for some applications since prediction will not be possible unless the minimal timespan of the data points is available. This is a problem for cyclone wind-intensity prediction, where prediction needs to be made as a cyclone is identified. This paper presents an empirical study on minimal timespan required for robust prediction using Elman recurrent neural networks. Two different training methods are evaluated for training Elman recurrent network that includes cooperative coevolution and backpropagation-though time. They are applied to the prediction of the wind intensity in cyclones that took place in the South Pacific over past few decades. The results show that a minimal timespan is an important factor that leads to the measure of robustness in prediction performance and strategies should be taken in cases when the minimal timespan is needed

Iowa State University, Courses and Programs Catalog 2014–2015

The Iowa State University Catalog is a one-year publication which lists all academic policies, and procedures. The catalog also includes the following: information for fees; curriculum requirements; first-year courses of study for over 100 undergraduate majors; course descriptions for nearly 5000 undergraduate and graduate courses; and a listing of faculty members at Iowa State University.https://lib.dr.iastate.edu/catalog/1025/thumbnail.jp