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How to Cite
Melby, J. A., Diop, F., Nadal-Caraballo, N., Taflanidis, A., & Gonzalez, V. (2018). HURRICANE WATER LEVEL PREDICTION USING SURROGATE MODELING. Coastal Engineering Proceedings, 1(36), currents.57. https://doi.org/10.9753/icce.v36.currents.57
Abstract
For this study, the surrogate was constructed using kriging (Jia et al. 2015). The high fidelity coupled surge and wave numerical modelling for the Gulf of Mexico was used as the training set. The numerical model was either ADCIRC and STWAVE or ADCIRC and SWAN in the nearshore. The surrogate models were trained using tropical storm parameters (latitude, longitude, central pressure, radius to maximum wind speed, storm heading, and forward speed) at a specific location as inputs and individual responses (e.g. surge) as outputs. Tide was computed separately using ADCIRC and linearly superimposed with surge to get total water level. The regional surrogates accurately reproduced both peaks and time series of water levels for historical storms. An extensive validation was conducted to determine the optimal application of the kriging approach. In this paper we will report the efficient design-of-experiments approach, surrogate training and validation.References
Jia, G., Taflanidis, A.A., Nadal-Caraballo, N.C., Melby, J.A., Kennedy, A.B., Smith, J.M., (2015). "Surrogate modelling for storm surge prediction using an existing database of synthetic storms; addressing time-dependence of output and implementation over an extended coastal region," Natural Hazards 81:909-938.
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