# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 2 Background
### Literature Review  
To enhance the safety of maritime transportation, previous research has examined the use of real-time data for route planning and navigational support. [PERSON] et al. [19] proposed a system that automatically generates collision-avoidance routes for autonomous ships using a domain-based Predictive Avoidance Domain (PAD), and visualizes the results on ENCs to support real-time decision-making by navigators. [PERSON] et al. [20] developed an intelligent route guidance system capable of dynamically steering vessels within temporarily restricted areas by integrating real-time data from Automatic Identification System (AIS), radar, and Closed Circuit Television (CCTV). [PERSON] et al. [21] analyzed multi-dimensional AIS data using a Density-Based Spatial Clustering Of Applications With Noise (DBSCAN)-based clustering algorithm, and visualized maritime traffic flow and density on ENCs to support both situational awareness and route planning. Similarly, [PERSON] et al. [22] proposed a real-time coastal navigation optimization framework for autonomous ships using a graph-based search algorithm that combines AIS information and ENC data to improve route safety. In addition to real-time sources, some studies have explored the use of historical data to improve navigational safety. [PERSON] et al. [23] linked AIS and radar data based on vessel trajectories in inland waterways to analyze navigational behavior and support intelligent navigation. [PERSON] and [PERSON] [24] used machine learning techniques to train models on AIS-based routes, enabling autonomous ships to emulate typical vessel behavior in generating safe navigation paths. Research on marine casualty data has also been conducted to support safety assessment and risk prediction. [PERSON] et al. [11] performed a spatial analysis of global marine casualty distributions, while [PERSON] et al. [12] proposed a model to predict casualty risk using AutoML techniques. While these studies have provided valuable insights into data-driven approaches to maritime safety, most research dealing with marine casualties has structured their data according to specific application needs. This practice often limits interoperability across platforms and reduces the potential for data reuse. Recent research has demonstrated how standardized datasets within the S-100 framework can be integrated to support various maritime applications. [PERSON] et al. [3] demonstrated that optimal route planning could be achieved by utilizing S-100-based data models for environmental and meteorological information. [PERSON] et al. [4] and [PERSON] et al. [5] also confirmed that S-100-based data integration is effective in improving both navigational decision-making and operational performance. [PERSON] et al. [13] emphasized that the open and extensible nature of the S-100 framework enables data integration not only for route planning, but also for broader maritime applications such as environmental monitoring and resource management. [PERSON] et al. [25] analyzed the design and implementation considerations of S-100-based data models from a software engineering perspective to promote consistency and system-level compatibility. [PERSON] et al. [26] conducted a comprehensive review of key trends in marine accident research, but the studies on data structuring or modeling were not included in their analysis. Despite these efforts, no standardized data model currently exists for representing marine casualty data within the S-100 framework. Most existing studies either treat such data in isolation or rely on non-standardized formats, which limits their reusability and integration into modern navigational systems. Therefore, to support risk-informed route planning and enable structured use of casualty data within digital navigation environments, there is a clear need for a data model that conforms to the principles of the S-100 standard.