Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22747-0_27 | COMPUTATIONAL SCIENCE - ICCS 2019, PT IV |
Field | DocType | Volume |
Information technology,Computer science,Visualization,Profiling (computer programming),Hydrography,Software,Continuous monitoring,Unsupervised learning,Artificial intelligence,Cluster analysis,Machine learning,Distributed computing | Conference | 11539 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David E. Losada | 1 | 326 | 40.63 |
| Pedro Montero | 2 | 0 | 0.34 |
| Diego Brea | 3 | 0 | 0.34 |
| Silvia Allen-Perkins | 4 | 0 | 0.34 |
| Begoña Vila | 5 | 0 | 0.34 |