Study for Filling Missing Wave Data in Geomundo Ocean Buoy Using Artificial Neural Networks

Abstract

This study aimed to propose an Artificial neural network (ANN) model to fill missing wave data using Bayesian optimization of hyperparameters. Ocean environmental data obtained by ocean buoys have been missed due to the malfunction or maintenance of monitoring system or extremely harsh weather condition during a storm. It is important of the continuity of measured data to analyze ocean environmental condition for the engineering purpose such as the design condition for offshore structure and the assessment of wave condition for a long term return period using the extreme analysis. Five ANN models were applied to estimate three wave parameters of significant wave height, peak wave period, and wave direction using of measurement data at Geomundo ocean buoy for eight years (2010–2017). The wind data of European Centre for Medium-Range Weather Forecasts were employed to estimate the wave parameters with ANN models to fill missing wave data at Geomundo ocean buoy. By comparison of each ANN model result, it could be suggested Bidirectional gated recurrent unit network, Gated recurrent unit network, Feed-forward neural network for the best model to fill the significant wave height, peak wave period and wave direction, respectively. These three ANN models could be applied to fill a long-term missing wave data at ocean buoys.

Keywords

• artificial neural network
• machine learning
• missing
• significant wave height
• peak wave period
• wave direction