Explainable artificial intelligence of machine and deep learning algorithms for multi-output prediction of wave characteristics

Abstract

Accurately predicting wave characteristics is essential for efficiently harnessing wave energy and ensuring safe maritime operations. This paper compares thirteen machine and deep learning algorithms to forecast wave characteristics using data from a buoy installation in Mooloolaba, Queensland, Australia. The approach diverges from tradition by making multi-output predictions across six wave characteristics, providing a more comprehensive understanding of wave behavior. In addition, it delves into the inner workings of the most effective models through explainable artificial intelligence, revealing the intricate mechanisms underlying their superior performance. The results showcase excellent model performance with minimal error values when dealing with multi-output regression challenges. The results underscore the remarkable potential of these algorithms to predict upcoming wave data on both short-term (30 min) and near-term (1-hour) horizons, allowing for timely intervention for nearshore device maintenance and activation of alert systems.