Neural Networks for Wave Height Interpolation

Abstract

Information on the heights of ocean waves at a site can be collected by a variety of instruments—each involving different methods of data retrieval and synthesis. Typically, sensing of wave heights by satellite requires the wave information to be presented in the form of values that are averaged over space and time intervals. In order to use such data for operational applications, it then becomes necessary to derive the wave heights over shorter intervals from their values available over long durations. This paper attempts to do this by employing the technique of neural networks. A simple three–layered feedforward network trained with the supervised backpropagation technique was used. The values of monthly mean significant wave heights available at different grid locations around the Indian coastline were given as input to obtain the output of weekly mean wave heights at the same locations. Analysis of the results indicated usefulness of the neural network technique in wave height interpolation problems.