Abstract
Understanding the spatial variation in ocean waves is critical for planning for erosion and infrastructure projects. The objectives of this study were to 1) perform a cluster analysis to categorize the behavior of wave climate over space and 2) determine the important drivers affecting spatial variations of wave climate in a low energy, fetch limited environment. In this study, 29 wave gauges were deployed between in Back Bay Biloxi, Mississippi. Raw pressure and processed wave height and period were clustered using two algorithms for calculating the similarity between timeseries data: Euclidian and Dynamic Time Warping. The Euclidean algorithm was applied to raw and processed data. However, due to the computationally expensive nature of Dynamic Time Warping, this algorithm could not be used on raw pressure data and was only applied to processed wave data. Therefore, three combinations of distance algorithms and data were compared to find the most effective way of clustering wave gauges over time and space: 1) Euclidean algorithm on raw pressure data, 2) Euclidean algorithm on processed wave height data, and 3) Dynamic Time Warping algorithm on processed wave height data. Results from this study reveal that the dendrogram trees of the Euclidean and Dynamic Time Warping algorithms on processed data are similar, where most of the wave gauges fall in one cluster. Conversely, the Euclidian algorithm on the raw pressure data resulted in wave gauges being slightly more evenly distributed between the clusters. Additionally, the Euclidean algorithm on the raw pressure data showed that water depth has an important influence on wave clustering and therefore, wave behavior.