## 3 Case Study Results  
In order to show the applicability and how to implement the novel method, a case study is performed. The study site is Kastela Bay. It is a semi-enclosed coast and the largestby situated in the Middle Adriatic. The bay is located at 43\({}^{\circ}\)32\({}^{\prime}\)00\({}^{\prime}\)N and 16\({}^{\circ}\)21\({}^{\prime}\)00\({}^{\prime}\)F [66]. It is 14.8 km long, approximately 6 km wide, and the total area is 61 km\({}^{2}\)[67; 68]. The bay is relatively shallow, with an average depth of 23 m. Maximum depth is 45 m at the bay's inlet. Heavy traffic across this area is under the Port Authority Split jurisdiction, consisting of passenger ports, trade ports, and a large number of ports and moorings for small boats [66]. Marina Kastela, in Kastel Gomilica with 420 moorings, is the large port of nautical tourism. It significantly contributes to the development of nautical tourism and density of maritime transport [69]. The seafloor consists of many different substrate types, including sand, rock, mud, and gravel [70]. In the proposed study case, the choice of alternatives is based on the technologies available by the Hydrographic Institute of the Republic of Croatia. Hence, those technologies would possibly be all available in the future if their purchase or lease proves profitable, as is shown in Table 3.  
Available technologies from Table 2 are considered with respect to the proposed distribution of characteristics and sub characteristics of the survey area from Table 1. Tables 1 and 3 show that the reduced binary elimination matrix is formed and expressed as shown in Table 4.  
Table 4 is filled with elements based on the previous section methodology. The mentioned literature was used to analyze the performance of the alternatives. The performances of hydrographic survey technologies are not uniform, but vary depending on the model and manufacturer. Therefore, in practice, these are data that are available from the manufacturer. Table 4 covers the relationship between available technologies' performances for the case study related to all possible survey area data. From Table 4, for example, in the case of technology UAV, it is evident survey coverage per day (characteristic A) is to a \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **ROV** & **AUV** & **LIDAR** & **SDB** & **UAV** & **MBES** \\ \hline \(a_{1}\) & 1 & 1 & 1 & 1 & 1 & 1 \\ \(a_{2}\) & 0 & 1 & 1 & 1 & 1 & 1 \\ \(a_{3}\) & 0 & 0 & 1 & 1 & 0 & 0 \\ \(a_{4}\) & 0 & 0 & 0 & 1 & 0 & 0 \\ \hline \(b_{1}\) & 0 & 0 & 1 & 0 & 1 & 0 \\ \(b_{2}\) & 1 & 0 & 1 & 1 & 1 & 0 \\ \(b_{3}\) & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \(c_{1}\) & 1 & 1 & 1 & 1 & 1 & 1 \\ \(c_{2}\) & 1 & 1 & 1 & 0 & 1 & 1 \\ \(c_{3}\) & 1 & 1 & 0 & 0 & 0 & 1 \\ \hline \(d_{1}\) & 1 & 1 & 1 & 1 & 1 & 0 \\ \(d_{2}\) & 0 & 0 & 1 & 1 & 1 & 1 \\ \(d_{3}\) & 0 & 0 & 1 & 1 & 1 & 0 \\ \hline \(e_{1}\) & 1 & 1 & 1 & 1 & 1 & 1 \\ \(e_{2}\) & 1 & 1 & 1 & 1 & 1 & 1 \\ \(e_{3}\) & 1 & 1 & 0 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Reduced binary elimination matrix for the study case. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Technology** \\ \hline Al\({}_{1}\) & ROV (remotely operated underwater vehicle) \\ Al\({}_{2}\) & UAV (unmanned aerial vehicle) \\ Al\({}_{3}\) & LIDAR (light detection and ranging) \\ Al\({}_{4}\) & AUV (autonomous underwater vehicle) \\ Al\({}_{5}\) & SDB (satellite derived bathymetry) \\ Al\({}_{6}\) & MBES (multibeam echosounder) \\ \hline \end{tabular}
\end{table} 
Table 3: Available technologies. maximum of 25 m (sub characteristics \(a_{1}\) and \(a_{2}\)). In terms of characteristic B, it can also be used in extremely shallow water (sub characteristics \(b_{1}\), \(b_{2}\), and \(b_{3}\)). The maximum depths (characteristic C) at which the UAV shows good performance are up to 45 m. Since its functionality does not include surface or underwater navigation, it can also be used when there is a possibility of hazards to sea navigation.