Storm Surge Prediction for Louisiana Coast Using Artificial Neural Networks

Multi-Output Artificial Neural Network for Storm Surge Prediction in North Carolina

During hurricane seasons, emergency managers and other decision makers need accurate and ‘on-time’ information on potential storm surge impacts. Fully dynamical computer models, such as the ADCIRC tide, storm surge, and wind-wave model take several hours to complete a forecast when configured at high spatial resolution. Additionally, statically meaningful ensembles of high-resolution models (ne...

Support vector regression methodology for storm surge predictions

To avoid property loss and reduce risk caused by typhoon surges, accurate prediction of surge deviation is an important task. Many conventional numerical methods and experimental methods for typhoon surge forecasting have been investigated, but it is still a complex ocean engineering problem. In this paper, support vector regression (SVR), an emerging artificial intelligence tool in forecasting...

Real-time Data Assimilation for Chaotic Storm Surge Model Using NARX Neural Network

Siek, M. and Solomatine, D.P., 2011. Real-time data assimilation for chaotic storm surge model using NARX neural network. Journal of Coastal Research, SI 64 (Proceedings of the 11th International Coastal Symposium), 1189 – 1194. Szczecin, Poland, ISSN 0749-0208 This paper introduces a real-time data assimilation technique where Nonlinear AutoRegressive with eXogenous inputs (NARX) neural networ...

A CYCLONE INDUCED STORM SURGE FORECASTING MODEL FOR THE COAST OF BANGLADESH WITH APPLICATION TO THE CYCLONE `SIDR'

The coast of Bangladesh has a specialty in terms of high bending and many off- shore islands. Incorporation of the coastline and island boundaries properly in the numerical scheme is essential for accurate estimation of water levels due to surge. For that purpose a numerical scheme consisting of very fine mesh is required along the coastal belt, whereas this is unnecessary away from the coast. In...

Prediction of Gas Lift Parameters Using Artificial Neural Networks