Abstract

Nowadays, the Convolution Neural Network(CNN) has been widely utilized to classify the image categories in many fields. We initially conducted some numerical trainings to figure out the applicability of machine learning in wave height prediction. A series of wave fields were produced with Airy wave super-positions. We constructed an efficient CNN structure which is able to tune various training parameters of machine learning algorithm. Also image filtering techniques were adopted and evaluated in wave field prediction. A long-crested wave case and some short-crested wave ones were modeled and trained in wave height classification. In the former case, the overall accuracy was remarkably high, but the prediction with short-crested waves were not reliable. While the middle range of wave height categories were not predictable, the highest and lowest wave height categories showed the good prediction results. Some variations on training parameters and structures were lastly performed in order to increase the prediction accuracy in short-crested wave trainings while their effectiveness was not significant.

INTRODUCTION

As the ocean environment has been widely utilized in building coastal structures, marine transportation, offshore engineering structures, aquaculture, and etc, the accurate identification and forecast of ocean conditions have been one of the critical issues including spectral representation of ocean waves. Ocean waves generate excessive kinetic energy periodically and are faced to man-made ships or infrastructures directly. While waves have a narrow-band spectrum mostly(DNVGL, 2019), they change their physical quantities slowly, and so accurate prediction of waves is quite difficult since numerous factors affect the wave generation mechanism in the ocean.

Various wave height gauges, which can be categorized in pressure-type, ultrasonic-type, microwave-type, probe-type, and so on, have been steadily developed. Also corresponding advanced instruments such as Acoustic Doppler Current Profiler(ADCP), Cross-Correlation Velocity Profiler(CCVP), Coherent Doppler Velocity Profiler (CDVP), and other ones have been installed near coastal lines around the world(Rijn, 2007).

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