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An efficient artificial intelligence model for prediction of tropical storm surge

Author

Listed:
  • M. Reza Hashemi

    (University of Rhode Island)

  • Malcolm L. Spaulding

    (University of Rhode Island)

  • Alex Shaw

    (University of Rhode Island)

  • Hamed Farhadi

    (Ferdowsi University of Mashhad)

  • Matt Lewis

    (Bangor University)

Abstract

Process-based models have been widely used for storm surge predictions, but their high computational demand is a major drawback in some applications such as rapid forecasting. Few efforts have been made to employ previous databases of synthetic/real storms and provide more efficient surge predictions (e.g. using storm similarity of an individual storm to those in the database). Here, we develop an alternative efficient and robust artificial intelligent model, which predicts the peak storm surge using the tropical storm parameters: central pressure, radius to maximum winds, forward velocity, and storm track. The US Army Corp of Engineers, North Atlantic Comprehensive Coastal Study, has recently performed numerical simulations of 1050 synthetic tropical storms, which statistically represent tropical storms, using a coupled high resolution wave–surge modeling system for the east coast of the US, from Cape Hatteras to the Canadian border. This study has provided an unprecedented dataset which can be used to train artificial intelligence models for surge prediction in those areas. While numerical simulation of a storm surge at this scale and resolution (over 6 million elements scaling from 20 m to more than 100 km) is extremely expensive, the artificial intelligence takes the advantage of the previous simulations, and effectively learns the relationship between storm parameters representing storm forcing and surge. The artificial neural network method which was used for this study, was shown to outperform support vector machine for extreme storms. ANN model, which is based on a neurobiological analogy, can be conveniently developed, retrained by new data, and is nonparametric. The AI model, which was developed for Rhode Island, was validated using a set of randomly selected synthetic storms as well as real tropical storms in this region. The model performance was found satisfactory with root-mean-square error of

Suggested Citation

  • M. Reza Hashemi & Malcolm L. Spaulding & Alex Shaw & Hamed Farhadi & Matt Lewis, 2016. "An efficient artificial intelligence model for prediction of tropical storm surge," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(1), pages 471-491, May.
    • Handle: RePEc:spr:nathaz:v:82:y:2016:i:1:d:10.1007_s11069-016-2193-4
    DOI: 10.1007/s11069-016-2193-4
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    References listed on IDEAS

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    1. Andrew Condon & Y. Peter Sheng, 2012. "Evaluation of coastal inundation hazard for present and future climates," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(2), pages 345-373, June.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. Matt Lewis & Kevin Horsburgh & Paul Bates, 2014. "Bay of Bengal cyclone extreme water level estimate uncertainty," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 983-996, June.
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    Cited by:

    1. S. Lecacheux & J. Rohmer & F. Paris & R. Pedreros & H. Quetelard & F. Bonnardot, 2021. "Toward the probabilistic forecasting of cyclone-induced marine flooding by overtopping at Reunion Island aided by a time-varying random-forest classification approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 227-251, January.

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