A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning  
[PERSON]  
Faculty of Maritime Studies, University of Split, Rudera Boskovica 37, 21000 Split, Croatia; [EMAIL_ADDRESS] (I.V.); [EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] (M.K.)  
to make the depth area comparable with depth areas of electronic charts [4]. Geospatial modeling of the tombolo phenomenon was addressed in [5], and measurements were carried out with an unmanned surface vehicle (USV). Measurements of the tombolo oceanographic phenomenon using UAV and USV were presented in [6]. The paper [7] presented the synergistic use of a small commercial UAV and a USV for beach rock mapping. A methodological proposal to generate a topobathymetric model, using low-cost unmanned platforms in a very shallow/shallow and turbid tidal environment was presented in [8]. Some references prefer ellipsoidal referenced surveying techniques (e.g., [9]) or satellite bathymetry methods [10].  
Current technical and technological solutions in the implementation of a hydrographic survey show a high degree of development. It is reasonable to assume that the cost is one of the main factors affecting the implementation of a hydrographic survey. The problem of costs in conducting a hydrographic survey has already been recognized in previous studies. Cost-effectiveness research has focused on new technologies and the time required for the implementation of the hydrographic survey [11; 12; 13; 14]. The impact of new technologies and computational techniques such as artificial intelligence is analyzed in [15; 16]. Satellite observation has been recognized to increase coverage by reducing the survey in shallow water [17].  
In such a variety of technologies, it is important to choose the right one for a specific area based on demands by international institutions, such as the International Hydrographic Organization (IHO). Such choice should include characteristics of the terrain, equipment, and internationally recognized demands. Requirements are not uniform, so the choice needs to be adjusted to the conditions. Interest in automatizing the learning process and non-manual decision tree was presented in [18]. Evolutionary algorithms for decision tree induction were reviewed. Evolutionary decision tree methods can be classified as full tree methods and methods that use tree components. The first uses classification regression (regression or model trees) and the former hyperplanes, pruning, and others. The genetic algorithms in combination with multicriteria decision (MCD) analysis were used in [19]. A benchmarking methodology for the optimal selection model was presented in [20].  
According to the available literature, no one has researched a complete approach in making the optimal survey technique solution. This paper aims to select the optimal technique while maintaining the required level of the hydrographic survey quality. The subject of this paper is to propose the model of identifying the appropriate technology. The model is based on the formulation of a binary elimination matrix, which serves as an input variable for the decision tree model creation. Finally, applying the weighted sum model (WSM) for optimization produces the optimal technology for the hydrographic survey.  
The proposed novel methodology is subject to complement as needed by hydrographer knowledge. Nonunified parameters, such as hydrographic survey area characteristics and technology performance, can be complemented. Additionally, the criteria that go into selecting optimal survey technologies are variable and depend on the required quality. Therefore, it is essential to emphasize how the proposed methodology is applicable in any survey area. In this paper, the proposed novel methodology is applied as a case study of KaStela Bay. The results of the study case, the advantages of the proposed methodology, and future research directions are detailed in the Results, Discussion, and Conclusion sections.