A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning

Abstract

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 paper, a novel 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: remotely operated underwater vehicle (ROV), unmanned aerial vehicle (UAV), light detection and ranging (LIDAR), autonomous underwater vehicle (AUV), satellite-derived bathymetry (SDB), and multibeam echosounder (MBES), and they are applied as a case study of Kaštela Bay. Results show, considering the specifics of the survey area, that UAV is the best-suited technology to be used for a hydrographic survey. However, some other technologies, such as SDB come close and can be considered an alternative for hydrographic surveys.

Keywords

• supervised learning
• decision tree
• hydrographic survey
• weighted sum model