## 4 Discussion and Conclusions  
The main contribution of this paper is the development of the hydrographic survey optimal technologies' selection model. First, it has to be pointed out that hydrographic surveys are performed in different survey areas. Further, each hydrographic survey area has an abundance of features to be considered. Additionally, all technologies are not suitable for every area. Hence, their performances are not universal and depend on the manufacturer, model, components, etc. Otherwise, the overall budget, urgency, and accuracy play an essential role. For example, some areas require an urgent hydrographic survey, then most often, the increase in implementation speed decreases the accuracy. Therefore, in addition to finding suitable technology for the survey area, the selected technology must satisfy the required hydrographic survey criteria. Since the paper proposes a novel methodology in selecting the optimal survey technology, it has to be emphasized that the method can be applied to all survey areas and all available technologies. The first step involves the obtained hydrographic survey area data. These are the primary data that the surveyor needs before surveying (such as min/max depth, seabed type, etc.). A reduced elimination binary matrix contains the distribution of characteristics and sub-characteristics of a hydrographic survey area to facilitate data management. It has to be pointed out that this matrix is the novelty of the proposed methodology and represents the input in the model. However, it is generally applicable and can be supplemented with new data as needed and assessed by a hydrographer. It has to be highlighted that the purpose of the matrix is to select suitable technologies that may be taken for further consideration with respect to the characteristics of the survey area. The performances of the technologies are available from the manufacturer. Given all the access data, the hydrographer has a simple task of binary filling the matrix. The most significant purpose is to make a decision tree based on binary data from the matrix. The decision tree's primary function is to create a model that can predict all possible alternatives depicted in Figure 5 as multicolor strips. Then, suitable technologies are further eliminated and graduated given their performance and required hydrographic survey quality. From the above discussion about the elimination matrix and a decision tree model, it is evident that the created decision tree model is done once. Addtionally, if a new technology becomes available and has different performances, it requires a new tree to be made. Applicability and validation of the proposed method is shown in the proposed study case. Kastela Bay represented the hydrographic area of interest. Binary values in the elimination matrix indicate the suitability of using all observed technologies concerning hydrographic survey characteristics of Kastela Bay. Based on the results of a matrix, ROV, UAV, LIDAR, AUV, SDB, and MBES were the appropriate technologies, as it is shown in Table 4.  
The binary data from the matrix represents an input for a decision tree model. The decision tree model is made using the following metrics the information gain ratio and Gini algorithms (Figure 6a,b). It has to be pointed out, using both metrics, the obtained decision tree models differ from each other with respect to the root node, i.e., tree depth, but they produce identical results. Therefore, the significant dissimilarity between the decision tree leave depths seeks to provide equal choice. Additionally, using a Gini algorithm for the training tree leads to a less complex and transparent decision tree (Figure 6b). Hence, all observed technologies are taken as available alternatives for choosing the optimal technological solution for Kastela Bay. Following the above discussion, multicriteria decision-making is performed by three criteria: cost, urgency, and accuracy.