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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doaj-edef9173690f460dac9c18301f916efe2021-04-17T23:02:27ZengMDPI AGAtmosphere2073-44332021-04-011251251210.3390/atmos12040512Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based ModelsFabio Di Nunno0Francesco Granata1Rudy Gargano2Giovanni de Marinis3Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, ItalyDepartment of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, ItalyDepartment of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, ItalyDepartment of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, ItalyThe 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.https://www.mdpi.com/2073-4433/12/4/512extreme eventstide forecastingartificial neural networkNARXVenice Lagoon |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fabio Di Nunno Francesco Granata Rudy Gargano Giovanni de Marinis |
spellingShingle |
Fabio Di Nunno Francesco Granata Rudy Gargano Giovanni de Marinis Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models Atmosphere extreme events tide forecasting artificial neural network NARX Venice Lagoon |
author_facet |
Fabio Di Nunno Francesco Granata Rudy Gargano Giovanni de Marinis |
author_sort |
Fabio Di Nunno |
title |
Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models |
title_short |
Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models |
title_full |
Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models |
title_fullStr |
Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models |
title_full_unstemmed |
Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models |
title_sort |
forecasting of extreme storm tide events using narx neural network-based models |
publisher |
MDPI AG |
series |
Atmosphere |
issn |
2073-4433 |
publishDate |
2021-04-01 |
description |
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. |
topic |
extreme events tide forecasting artificial neural network NARX Venice Lagoon |
url |
https://www.mdpi.com/2073-4433/12/4/512 |
work_keys_str_mv |
AT fabiodinunno forecastingofextremestormtideeventsusingnarxneuralnetworkbasedmodels AT francescogranata forecastingofextremestormtideeventsusingnarxneuralnetworkbasedmodels AT rudygargano forecastingofextremestormtideeventsusingnarxneuralnetworkbasedmodels AT giovannidemarinis forecastingofextremestormtideeventsusingnarxneuralnetworkbasedmodels |
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1721523696710975488 |