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)


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.


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.


  1. 1.
    Jelesnianski, C.P., Chen, J., Shaffer, W.A.: SLOSH: Sea, Lake, and Overland Surges from Hurricanes. Silver Springs, Maryland (1992)Google Scholar
  2. 2.
    Luettich, R.A., Westerink, J.J., Scheffner, N.W.: ADCIRC: an advanced three-dimensional circulation model for shelves, coasts and estuaries, Report 1: theory and methodology of ADCIRC-2DDI & ADCIRC-3DL (1992)Google Scholar
  3. 3.
    Chen, C., Liu, H., Beardsley, R.C.: An unstructured grid, finite-volume, three-dimensional, primitive equations ocean model: application to coastal ocean and estuaries. J. Atmos. Ocean. Tech. 20(1), 159–186 (2003)CrossRefGoogle Scholar
  4. 4.
    Shchepetkin, A.F., McWilliams, J.C.: The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following coordinate oceanic model. Ocean Model 9(4), 347–404 (2004)CrossRefGoogle Scholar
  5. 5.
    Lesser, G.R., Roelvink, J.A., Van Kester, J.A.T.M., Stelling, G.S.: Development and validation of a three-dimensional morphological model. Coast. Eng. 51(8), 883–915 (2004)CrossRefGoogle Scholar
  6. 6.
    Lee, T.: Prediction of storm surge and surge deviation using a neural network. J. Coast. Res. 24, 76–82 (2008)CrossRefGoogle Scholar
  7. 7.
    You, S., Seo, J.: Storm surge prediction using an artificial neural network model and cluster analysis. Nat. Hazards 51(1), 97–114 (2009)CrossRefGoogle Scholar
  8. 8.
    De Oliveira, M.M.F., Ebecken, N.F.F., De Oliveira, J.L.F., De Azevedo Santos, I.: Neural network model to predict a storm surge. J. Appl. Meteorol. Climatol. 48, 143–155 (2009)CrossRefGoogle Scholar
  9. 9.
    Bajo, M., Umgiesser, G.: Storm surge forecast through a combination of dynamic and neural network models. Ocean Model 33, 1–9 (2010)CrossRefGoogle Scholar
  10. 10.
    The ADvanced CIRCulation model (ADCIRC).
  11. 11.
    Luettich, R., Westerink, J.: Formulation and numerical Implementation of the 2D/3D ADCIRC finite element model version 44.XX (2004).
  12. 12.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)CrossRefzbMATHGoogle Scholar
  13. 13.
  14. 14.
  15. 15.
  16. 16.
  17. 17.
    Bunya, S., Dietrich, J.C., Westerink, J.J., Ebersole, B.A., Smith, J.M., Atkinson, J.H., Jensen, R., Resio, D.T., Luettich, R.A., Dawson, C., Cardone, V.J., Cox, A.T., Powell, M.D., Westerink, H.J., Roberts, H.J.: Observation of strains: a high-resolution coupled riverine flow, tide, wind, wind wave, and storm surge model for southern louisiana and mississippi. Part I: model development and validation. Mon. Wea. Rev. 138, 345–377 (2011). doi: 10.1175/2009MWR2906.1 CrossRefGoogle Scholar

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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