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.
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Wang, Q., Chen, J., Hu, K. (2016). Storm Surge Prediction for Louisiana Coast Using Artificial Neural Networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_43
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DOI: https://doi.org/10.1007/978-3-319-46675-0_43
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