Coastal zone significant wave height prediction by supervised machine learning classification algorithms.

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. • Structural acceleration data can be leveraged to obtain better classification models, aiding the prediction of significant wave height. • Neural networks and tree ensemble classification algorithms are effective in the prediction of significant wave height. • Network complexity and structural feature redundancy increases chances of overfitting, compromising model generalization. • Gini impurity index is a viable feature importance indicator, aiding the robustness of the models post feature curtailement. [ABSTRACT FROM AUTHOR]