## 4 Discussion and Conclusions
First, the cost has been selected as the preferred criteria for the study, then the criterion urgency follows, and the least important measure is accuracy for the Kastela Bay study case (Table 6). Further, applying WSM, the numerical results, which have ranked technologies for the hydrographic survey, are obtained (Table 8). The optimal technology for the Kastela Bay study case was the UAV, with a score of 0.92. Then, with a score of 0.90 follows SDB technology as the best alternative to UAV. Additionally, ithas to be noticed that the MBES technology had the lowest score of 0.53 and represents the least favorable alternative to be used for a hydrographic survey of Kastela Bay. For other considered technologies, no evident difference has been observed. Furthermore, if the costs were the only decision criterion, after applying our algorithm, only the cheapest alternative would be selected as the best technology, which may not be as efficient and effective as other technologies. Hence, such an approach would represent a constraint in practice. Therefore, it is not recommended to select optimal technologies to be guided solely by cost as the only decision criterion. The time factor was also considered an important criterion in a hydrographic survey and presents the second criterion in importance and had to be included in a multi-decision optimization. Based on Table 7 from study case results, it is evident if decision-making is based on two equally important criteria, cost and urgency, then ROV, UAV, and AUV would be selected as optimal technologies. However, by such a decision, the accuracy is completely neglected. For example, suppose the time is not an issue, and the costs and accuracy are equally important in decision making. In that case, SDB will be the optimal choice for represented study case. Likewise to the above, when funds are sufficient, and urgency and accuracy are the only and equally important criteria, the optimal technology choice for Kastela Bay would be LIDAR. In the end, it has to be highlighted the following: parameters that represent the input to the model are the reduced binary elimination matrix and the optimization criteria. In addition, the binary matrix is obtained on the basis of available data of the survey area (Table 1) and available technologies (e.g., Table 3). Further, optimization criteria are determined based on the financial budget, the time available, and the desired level of accuracy. Finally, all mentioned elements in the matrix and optimization criterion are filled by a hydrographic using the proposed methodology and available data. Future research direction includes other multi-decision methods that could be investigated on this subject. Additionally, it is necessary to compare them to conclude on the most appropriate. Further, the limitations of the proposed method should be mentioned. For example, the financial budget of some countries does not allow consideration of more than two/three technologies. In addition, it has to be noted that, currently, that from the available literature, there are no similar developed algorithms at present. Conceptualization, I.G.M., I.V. and J.S. ; methodology, I.G.M. ; validation, I.G.M., J.S. and I.V. ; formal analysis, I.G.M. and M.K. ; resources, I.G.M. ; data curation, I.G.M., I.V. and J.S. ; writing--original draft preparation, I.V., J.S., I.G.M. and M.K. ; visualization, I.G.M. ; supervision, J.S., I.V. and I.G.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.