http://www.cnr.it/ontology/cnr/individuo/prodotto/ID49786
Storm surge forecast through a combination of dynamic and neural network models (Articolo in rivista)
- Type
- Label
- Storm surge forecast through a combination of dynamic and neural network models (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.ocemod.2009.12.007 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Bajo, M.; G. Umgiesser (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- Scopu (literal)
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- 1. Institute of Marine Science - National Research Council (ISMAR-CNR), Castello 1364/A, 30122, Venice, Italy (literal)
- Titolo
- Storm surge forecast through a combination of dynamic and neural network models (literal)
- Abstract
- An operational surge forecast system based on a combination of a hydrodynamic model and an artificial neural network is described. The system runs at the Centre for sea level forecasting and flood warnings of the Venice Municipality (ICPSM, Istituzione Centro Previsioni e Segnalazioni Maree) and is focused on the prediction of the surge near Venice. The hydrodynamic model provides a five-day forecast for the Mediterranean Sea. Then, results near Venice are extracted and improved using a neural network. The method exposed in this paper reduced by half the average error of the hydrodynamic model for the first day forecast and maintains good performances also for longer forecasts. Moreover it is able to reduce the bias error of the model. (literal)
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