## 3 Case Study Results
Hence, a value of 1 belongs to all sub characteristic (\(d_{1}\), \(d_{2}\), \(d_{3}\)) of characteristic D. Analogously, UAV can work even when the seabed is rocky or coral, soft or covered with vegetation (sub characteristic \(e_{1}\), \(e_{2}\), and \(e_{3}\)). It has to be noted that the reduced binary elimination matrix represents the input variable for decision tree creation. Hence, using the matrix from Table 4 represents a set of training data in the decision tree working process. Based on these data, a general decision tree model was created with the software package RapidMiner version 9.8. It is one of the most used open-source predictive analytics platforms utilized for data analysis. It is accessible as a stand-alone application for information investigation and a data mining engine to integrate into its products. RapidMiner provides an integrated environment for data mining and machine learning procedures [71]. It has to be pointed out that the decision tree represents all possible combinations with used technologies. Figure 5 shows a decision tree model using the information gain ratio algorithm for a considered study case based on Table 4.  
From Figure 5, it has to be noted that sometimes individual characteristics and sub-characteristics denote only one technology, and sometimes they refer to several of them. Additionally, from Figure 5, it can be seen that multicolors strips denote several alternatives that are possible at the same instance. Further, the name of the terminal node technology depends on the data used for tree training and the probability of that technology as an output value. Hence, multicolors strips of nodes explain the possibility of choosing several technologies. For example, according to Table 1 and Figure 5, if the maximum depth of survey area (characteristic C) from less than or equal to 25 m (sub characteristic \(c_{1}\)) and the minimum depth to be surveyed (characteristic B) less than or equal to 1 m (sub characteristic \(b_{1}\)), the optimal technologies are LIDAR and UAV. Characteristics of the considered area-Kastela Bay are given in Table 5, including survey area (parameter A), depth (parameters B and C), and seabed type (parameter E). Figure 5: Decision tree using information gain ratio algorithm. It has to be pointed out, the parameter D is excluded from the consideration since it is known that in the Kastela Bay area, there is no possibility of hazards to operation. It is worthwhile to mention that this study's area of interest was obtained from the general tree in Figure 5 and is shown in Figure 6a,b. Figure 6a,b confirms the state from the reduced binary elimination matrix that all observed technologies are suitable for hydrographic surveys in Kastela Bay. Therefore, they are taken as available alternatives for choosing the optimal solution. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Characteristic and Sub Characteristic of the Kastela Bay**} \\ \hline A—survey area & B—the minimum & C—the maximum & E—seabed type of area \\ coverage per day & depth to be survey & depth of survey area & E—seabed type of area \\ \hline \(a_{1}\)—\(\leq 1\) km\({}^{2}\)/day & \(b_{3}\)—6–20 m & \(c_{3}\)—45 m & \(e_{1}+e_{2}\)—mixed type \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Characteristic and sub characteristic of the hydrographic survey area. Figure 6: A tree decision model for Kastela Bay study case: (**a**) using information gain ratio algorithm; (**b**) using Gini algorithm. Furthermore, to perform optimization and determine the best-suited technology for the Kastela Bay case study, three criteria are performed in multicriteria decision-making: cost (C\({}_{1}\)), urgency (C\({}_{2}\)), and accuracy (C\({}_{3}\)). Hence, the WSM method's procedure was conducted. Table 6 shows the relationship between the given criteria, weights, and consistency vector. Table 6 is derived using Equations (10)-(12) from the methodology section.