Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models

The extreme values of high tides are generally caused by a combination of astronomical and meteorological causes, as well as by the conformation of the sea basin. One place where the extreme values of the tide have a considerable practical interest is the city of Venice. The MOSE (MOdulo Sperimental...

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Bibliographic Details
Published inAtmosphere Vol. 12; no. 4; p. 512
Main Authors Di Nunno, Fabio, Granata, Francesco, Gargano, Rudy, de Marinis, Giovanni
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2021
Subjects
artificial neural network
Astronomical tides
Atmospheric pressure
Coasts
Conformation
extreme events
Extreme values
Extreme weather
Flooding
Floods
Forecasting
High tide
Hydrology
NARX
Neural networks
Rivers
Sea level
Storm forecasting
Storm tides
Storms
Tidal waves
tide forecasting
Tides
Time series
Venice Lagoon
Weather
Weather conditions
Wind speed
Australia
United States > US
Bay of Fundy
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Summary:The extreme values of high tides are generally caused by a combination of astronomical and meteorological causes, as well as by the conformation of the sea basin. One place where the extreme values of the tide have a considerable practical interest is the city of Venice. The MOSE (MOdulo Sperimentale Elettromeccanico) system was created to protect Venice from flooding caused by the highest tides. Proper operation of the protection system requires an adequate forecast model of the highest tides, which is able to provide reliable forecasts even some days in advance. Nonlinear Autoregressive Exogenous (NARX) neural networks are particularly effective in predicting time series of hydrological quantities. In this work, the effectiveness of two distinct NARX-based models was demonstrated in predicting the extreme values of high tides in Venice. The first model requires as input values the astronomical tide, barometric pressure, wind speed, and direction, as well as previously observed sea level values. The second model instead takes, as input values, the astronomical tide and the previously observed sea level values, which implicitly take into account the weather conditions. Both models proved capable of predicting the extreme values of high tides with great accuracy, even greater than that of the models currently used.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos12040512