This article deals with the development and application of a risk analysis approach regarding the importance and fate of shipwrecks in Greece. In the past few decades the potential harmful effects of wrecks and the materials and substances they carry, remaining on the sea bottom, have been realized and the issue of dealing with them has arisen. The methodology used in this article consists of three steps. The first step is to develop a dedicated database of shipwrecks in Greek waters. The second step focuses on the quasi-quantitative assessment of risk for each shipwreck individually through the introduction of implemented factors that have been modeled into the structure of a hierarchical fuzzy system. The analysis is concluded by determining the sources of uncertainty for the system and implementing an appropriate analysis to determine their relative importance. Finally, geographic information system (GIS) tools are used for the visualization of data and the analysis of results in geographic terms as well as for revealing geographic patterns and relationships.

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In case of a ship emergency situation and during its evolvement that might result in an evacuation, the master and the bridge command team of a ship have to continuously assess risk. This is a very complex procedure, as crucial decisions concerning safety are made under time pressure. The use of a decision-support tool would have a positive effect on their performance, resulting in an improvement in the way ships are evacuated. The purpose of this paper is to present the PALAEMON smart risk assessment platform (SRAP). SRAP is a real-time risk assessment platform developed to assist the decision-making process of the master and bridge command team of a ship regarding the evacuation process. Its purpose is to provide decision support for the following aspects: (1) the decision to sound the general alarm (GA) following an accident, (2) monitoring the progress of the mustering process in order to take any additional actions, and (3) the decision to abandon the ship or not. SRAP dynamically assesses the risk to the safety of the passengers and crew members in the different phases of the evacuation process, so one model in the form Bayesian networks (BNs) was developed for each stage of the evacuation process. The results of a case study that was implemented reflect how various parameters such as injuries, congestion, and the functionality of the ship’s systems affect the outcome of each model.

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