Filling Missing and Extending Significant Wave Height Measurements Using Neural Networks and an Integrated Surface Database

Abstract

Wave data play a critical role in offshore structure design and coastal vulnerability studies. For various reasons, such as equipment malfunctions, wave data are often incomplete. Despite the interest in completing the data, few studies have considered constructing a machine learning model with publicly available wind measurements as input, while wind data from reanalysis models are commonly used. In this work, ANNs are constructed and tested to fill in missing wave data and extend the original wave measurements in a basin with limited fetch where wind waves dominate. Input features for the ANN are obtained from the publicly available Integrated Surface Database (ISD) maintained by NOAA. The accuracy of the ANNs is also compared to a state-of-the-art reanalysis wave model, MEDSEA, maintained at Copernicus Marine Service. The results of this study show that ANNs can accurately fill in missing wave data and also extend beyond the measurement period, using the wind velocity magnitude and wind direction from nearby weather stations. The MEDSEA reanalysis data showed greater scatter compared to the reconstructed significant wave heights from ANN. Specifically, MEDSEA showed a 22% higher HH index for expanding wave data and a 33% higher HH index for filling in missing wave data points.