Pregled bibliografske jedinice broj: 874639
Data based modelling of the mean wave period in the Adriatic Sea
Data based modelling of the mean wave period in the Adriatic Sea // International Maritime Science Conference-Book of proceedings / Vidan, Pero ; Račić, Nikola ; Twrdy, Elen ; Bukljaš Skočibušič, Mihaela ; Radica, Gojmir ; Vukić, Luka ; Mudronja, Luka (ur.).
Split: Pomorski fakultet Sveučilišta u Rijeci, 2017. str. 308-318 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Data based modelling of the mean wave period in
the Adriatic Sea
Autori
Katalinić, Marko ; Mudronja, Luka ; Matić, Petar
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
International Maritime Science Conference-Book of proceedings
/ Vidan, Pero ; Račić, Nikola ; Twrdy, Elen ; Bukljaš Skočibušič, Mihaela ; Radica, Gojmir ; Vukić, Luka ; Mudronja, Luka - Split : Pomorski fakultet Sveučilišta u Rijeci, 2017, 308-318
Skup
International Maritime Science Conference
Mjesto i datum
Solin, Hrvatska, 20.04.2017. - 21.04.2017
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
sea wave modelling ; Adriatic Sea ; data based modelling ; mean wave period ; Matlab
Sažetak
This paper investigates an ability to apply data based modelling methods for mean wave period modelling in a single point in the Adriatic Sea, while examining the influence of different input variables and their respective time series. For that purpose, regression analysis and artificial neural network were used. Total of 22-year data with 6h step size, i.e. 33604 data samples acquired from a satellite calibrated numeric model, were used to form the models. Available data set was divided in two subsets, 20-year data, i.e. 30684 data samples, which were used to calibrate the models, and 2-year data, i.e. 2920 data samples, that were used to test the model performance. Simulations were performed in Matlab, with the results proving the efficiency of modelling approaches, where artificial neural network provided more accurate results than traditional statistical models. Furthermore, the advantage of the neural network was more prominent for the case of multiple input variables.
Izvorni jezik
Engleski
Znanstvena područja
Brodogradnja, Elektrotehnika
POVEZANOST RADA
Ustanove:
Pomorski fakultet, Split