A Comparative Analysis of Machine Learning Models for Prediction of Wave Heights in Large Waterbodies

Sinha, Priyanka and Vincent, Shweta and Kumar, Om Prakash (2018) A Comparative Analysis of Machine Learning Models for Prediction of Wave Heights in Large Waterbodies. International Journal of Engineering and Technology(UAE), 7 (4.41). pp. 91-95. ISSN 2227-524X

[img] PDF
5689.pdf - Published Version
Restricted to Registered users only

Download (579kB) | Request a copy

Abstract

This paper presents a study of the various machine learning algorithms viz. Linear Regression, Logistic Regression, Support Vector Machine, Support Vector Regression and Extreme Machine Learning for the prediction of wave heights using data obtained from ocean buoys. The data from the ocean buoy number 62081 off the coast of Ireland in Europe has been chosen for study. It is found that the parameter of wind speed affects wave heights the most in comparison to other parameters. It is also observed that Extreme Learning Machine outperforms Support Vector Regression when classifying the data points as high tide or low tide. The MSE and CC parameters prove the suitability of Extreme Machine Learning over all the other algorithms discussed in this paper for the accurate prediction of wave heights

Item Type: Article
Uncontrolled Keywords: Linear Regression, Logitstic Regression, Support Vector Machine, Support Vector Regression, Extreme Learning Machine
Subjects: Engineering > MIT Manipal > Electronics and Communication
Engineering > MIT Manipal > Mechatronics
Depositing User: MIT Library
Date Deposited: 09 Jan 2019 09:12
Last Modified: 09 Jan 2019 09:12
URI: http://eprints.manipal.edu/id/eprint/152816

Actions (login required)

View Item View Item