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Storm Surge Prediction for Louisiana Coast Using Artificial Neural Networks

  • Qian Wang
  • Jianhua ChenEmail author
  • Kelin Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

Storm surge, an offshore rise of water level caused by hurricanes, often results in flooding which is a severe devastation to human lives and properties in coastal regions. It is imperative to make timely and accurate prediction of storm surge levels in order to mitigate the impacts of hurricanes. Traditional process-based numerical models for storm surge prediction suffer from the limitation of high computational demands making timely forecast difficult. In this work, an Artificial Neural Network (ANN) based system is developed to predict storm surge in coastal areas of Louisiana. Simulated and historical storm data are collected for model training and testing, respectively. Experiments are performed using historical hurricane parameters and surge data at tidal stations during hurricane events from the National Oceanic and Atmospheric Administration (NOAA). Analysis of the results show that our ANN-based storm surge predictor produces accurate predictions efficiently.

Keywords

Artificial Neural Network Artificial Neural Network Model Storm Surge Maximum Wind Speed Forward Speed 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Division of Computer Science and EngineeringLouisiana State UniversityBaton RougeUSA
  2. 2.Center for Computation and TechnologyLouisiana State UniversityBaton RougeUSA

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