A Method of Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning (CROSBI ID 442490)
Ocjenski rad | doktorska disertacija
Podaci o odgovornosti
Golub Medvešek, Ivana
Dodig, Hrvoje ; Leder, Nenad
engleski
A Method of Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning
Hydrographic survey or seabed mapping plays an important role in achieving better maritime safety, especially in coastal waters. Due to advances in survey technologies, it becomes important to choose well-suited technology for a specific area. Moreover, various technologies have various ranges of equipment and manufacturers, as well as characteristics. Therefore, in this thesis, a proposed method of a hydrographic survey, i.e., identifying the appropriate technology, has been developed. The method is based on a reduced elimination matrix, decision tree supervised learning, and multicriteria decision methods. The available technologies were: SBES (research vessel), SBES+SSS (research vessel), MBES (research vessel), MBES (research vessel)+SBES (small boat), LIDAR (UAV), SDB (satellite sensors) and they are applied as a case study of Kaštela Bay. The optimal technology for Kaštela Bay study case was MBES (research vessel) and MBES (research vessel) + SBES (small boat) with a score of 0.97. Then with a score of 0.82 follows the SDB technology. Other available alternatives have a significantly lower score. It is a small evident difference between the three alternatives SBES (research vessel), SBES+SSS (research vessel), and LIDAR, which have a WSM score in the range from 0.58 – 0.65.
International Hydrographic Organisation, survey categories, IHO regions, stranding, supervised learning, decision tree, hydrographic survey, weighted sum model, multidecision, criteria, survey technologies
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Podaci o izdanju
96
16.07.2021.
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