Nowcasting significant wave height by hierarchical machine learning classification

Abstract

This paper proposes an alternative method for nowcasting significant wave height (Hs) through the development of hierarchical machine learning classification models. In testing the hypothesis that hierarchical classification can improve Hs prediction, flat and hierarchical classifiers were developed and tested on field-data recorded on a coastal jetty located in the southern coasts of Cyprus. A comprehensive investigation of the performance of flat over hierarchical classification models yields that the proposed method provides greater flexibility throughout the model development stages. This flexibility is attributed to the manipulation of data before training, optimization of classifier's hyperparameters during training, and the curtailment of features post-training at each level of the hierarchy. It is demonstrated that, the hierarchical approach resulted in better classification performance across a plethora of performance metrics established for a comprehensive comparison. It is also shown that the increased performance of the proposed approach comes at the expense of complexity arising from performing computationally expensive operations and the requirement for development of multiple local classifiers. Still, the increased classification performance of the hierarchical approach highlights the potential of this original method and the requirement for a rigid framework to be constructed for the development of hierarchical models for Hs prediction.