Neural network approach to sea-level modeling case study of a storm surge in the Gulf of Trieste in early December 2008

Authors: Matjaž LIČER, Dušan ŽAGAR, Maja JEROMEL, Jure JERMAN
Citation: Acta hydrotechnica, vol. 24, no. 41, pp. 27-45, 2006.
Abstract: Tide tables can be a useful tool for sea-level forecasting in many areas. Slovenian operational service for hydrological forecasts at the Environmental Agency of the Republic of Slovenia frequently deploys tide tables alongside least square harmonic analysis to predict maximum sea levels in the Gulf of Trieste. Meteorological influences such as pressure gradient, wind stress and induced basin eigenoscillations (seiches) along the main axis of the Adriatic basin have repeatedly been proven as important factors influencing the sea level in the Gulf of Trieste. They are, however, only indirectly included in the harmonic analysis which in itself requires a large number of carefully tuned model parameters in order to make useful short-range forecasts. A number of recent reports show that an artificial neural network (ANN) can greatly improve sea level forecasts, providing we supply it with suitable input variables (ie. previous water levels, air pressure, wind speed, wind direction, tide charts etc.) We report on an ANN-based analysis of the recent storm surge and flooding events at the Slovenian coast in the beginning of December 2008. The ANN model compares favourably with the currently used conventional forecasting methods.
Keywords: neural networks, sea-level forecasting, harmonic analysis, Adriatic Sea, storm surge
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