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Title: Coastal zone significant wave height prediction by supervised machine learning classification algorithms
Authors: Demetriou, Demetris 
Michailides, Constantine 
Papanastasiou, George 
Onoufriou, Toula 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Machine learning;Significant wave height prediction;Neural networks;Classification algorithms;Gini impurity index;Decision tree
Issue Date: 1-Feb-2021
Source: Ocean Engineering, 2021, vol. 221, no. 1
Volume: 221
Issue: 1
Journal: Ocean Engineering 
Abstract: Explicit wave models and expensive sensor equipment capable of predicting and measuring wave parameters often carry a prohibitive computational and financial expense. To counter this, this paper proposes an alternative method for nowcasting coastal zone significant wave heights through the joint use of meteorological and structural data in the training of supervised machine learning models. In testing the hypothesis that structural data can improve model classification, artificial neural network and decision tree models were developed, trained and tested on field data recorded on a coastal jetty located in the southern coasts of Cyprus. A comprehensive investigation of the different models yields that the joint use of meteorological and structural features can improve classification performance, regardless of the network choice. It is also demonstrated that redundancy of training parameters could inject unwanted overfitting, reducing model generalization. To address this, a method for quantifying feature importance has been proposed by exploiting the nature of decision tree algorithms and the Gini impurity index, reaffirming that structural features do indeed benefit model classification. These results highlight the potential of tapping into the untapped pool of structural data for significant wave height prediction, paving the way for new research to be undertaken in this direction.
ISSN: 0029-8018
DOI: 10.1016/j.oceaneng.2021.108592
Type: Article
Affiliation : Cyprus University of Technology 
VTT Vasiliko Ltd 
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