Pregled bibliografske jedinice broj: 889427
Application of neural networks and support vector machine for significant wave height prediction
Application of neural networks and support vector machine for significant wave height prediction // Oceanologia, 59 (2017), 3; 331-349 doi:10.1016/j.oceano.2017.03.007 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 889427 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Application of neural networks and support vector machine for significant wave height prediction
Autori
Berbić, Jadran ; Ocvirk, Eva ; Carević, Dalibor ; Lončar, Goran
Izvornik
Oceanologia (0078-3234) 59
(2017), 3;
331-349
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Significant wave height ; Wave prediction ; Machine learning ; ANN ; SVM
Sažetak
For the purposes of planning and operation of maritime activities, information about wave height dynamics is of great importance. In the paper, real-time prediction of significant wave heights for the following 0.5–5.5 h is provided, using information from 3 or more time points. In the first stage, predictions are made by varying the quantity of significant wave heights from previous time points and various ways of using data are discussed. Afterwards, in the best model, according to the criteria of practicality and accuracy, the influence of wind is taken into account. Predictions are made using two machine learning methods – artificial neural networks (ANN) and support vector machine (SVM). The models were built using the built-in functions of software Weka, developed by Waikato University, New Zealand.
Izvorni jezik
Engleski
Znanstvena područja
Građevinarstvo
POVEZANOST RADA
Ustanove:
Državni hidrometeorološki zavod,
Građevinski fakultet, Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus