Congress 1252
  • David E. Losada, Pedro Montero, Diego Brea, Silvia Allen-Perkins, Begoña Vila
  • 19th International Conference on Computational Science. Faro, Portugal. 2019

Clustering Hydrographic Conditions in Galician Estuaries

In this paper we describe our endeavours to explore the role of unsupervised learning technology in profiling marine conditions. The characterization of the marine environment with hydrographic variables allows, for example, to make technical and health control of sea products. However, the continuous monitoring of the environment produces large amounts of data and, thus, new information technology tools are needed to support decision-making. We present here a first contribution to this area by building a tool able to represent and normalize hydrographic conditions, cluster them using unsupervised learning methods, and present the results to domain experts. The tool, which implements visualization methods adapted to the problem at hand, was developed under the supervision of specialists on monitoring marine environment in Galicia (Spain). This software solution is promising to early identify risk factors and to gain a better understanding of sea conditions.
Keywords: Marine Conditions, Machine Learning, Unsupervised Learning, Clustering, Hydrographic Conditions
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