Application of neural networks and support vector machine for significant wave height prediction

• Autorzy: Berbic J. , Ocvirk E. , Carevic D. , Loncar G.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

wave height; prediction; artificial neural network; support vector machine; machine learning

• Wydawca: -
• Czasopismo: Oceanologia
• Rocznik: 2017
• Tom: 59
• Numer: 3
• Opis fizyczny: p.331-349,fig.,ref.

Twórcy

autor

Berbic J.

• Croatian Hydrological and Meteorological Service, Zagreb, Croatia

autor

Ocvirk E.

• The Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia

autor

Carevic D.

• The Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia

autor

Loncar G.

• The Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia

Identyfikatory

DOI

10.1016/j.oceano.2017.03.007