## 4 Results II--Review of Research Clusters
### Cluster 1: Risk/Accident Analysis
#### 4.2.1 Sub-Cluster 1: Accident Analysis
(2016) [40] applied multiple regression models, including Negative Binomial Regression, Zero-Inflated Negative Binomial Regression (ZNBR), and Fractional Logit Regression (FLR), to analyze accident occurrence rates and identify key contributing factors. Their results revealed that wind speed, ice presence, and pressure changes were the most influential factors. While these models are effective for handling diverse data characteristics and simultaneously analyzing accident probability and frequency, the study suggested that hierarchical or multi-level modeling could offer a more refined approach by reducing data complexity. [PERSON] and [PERSON] (2020) [53] employed a case-control research design, analyzing the relationship between risk factors and large-scale disaster events, like capsizing and sinking. Logistic regression was used to evaluate the odds of disaster occurrence, and a multivariable model was applied to analyze the simultaneous impact of all variables. The methodology demonstrated its ability to generate meaningful results even from relatively small sample sizes but faced challenges in controlling for certain variables, potentially leading to bias. To address this, the study recommended controlling for confounding factors such as weather conditions and sea states. Statistical research based on accident data has also been conducted. [PERSON] et al. (2005) [16] used frequency analysis by accident type and statistical analysis by cause to evaluate the main causes of accidents and the contribution of each type. Their findings indicated that inadequate vessel maintenance, equipment failure, navigational errors, and lack of proper navigation equipment were primary causes of accidents. Given that machinery damage accounted for the majority of accidents, they recommended strengthening vessel maintenance programs and implementing additional management measures to prevent mechanical failures. [PERSON] et al. (2013) [31] also employed the statistical method using Incident Rate per 10,000-man years to assess accident severity and mortality rates, representing the fatal accident rate relative to the total workforce in the fishing industry. They found that vessel disasters, such as capsizing, sinking, and grounding, accounted for the highest proportion of fatalities, while man-overboard incidents were also significant. The study highlighted that small coastal vessels have a particularly high accident risk and emphasized the importance of effective safety policies and preventive measures. An example of the latest techniques being applied to statistical analysis, [PERSON] et al. (2023) [57] employed statistical analysis and spatial distribution visualization using the Kernel Density Estimation (KDE) method to identify areas with a high concentration of collision incidents. This approach, based on actual data, provides an objective basis for analysis and is advantageous for visual interpretation. However, it heavily relies on data accuracy, making it challenging to discern the exact causes and timing of incidents. To address these limitations, integrating this method with Geographic Information System (GIS) technology can enable a clearer visual representation of distribution and risk levels. There is also a study using the evaluation methods of the IMO. [PERSON] et al. (2020) [48] used the Formal Safety Assessment (FSA) methodology, which involves risk analysis and cost-benefit assessment to examine various risk control options. Accident types were categorized into vessel foundering, falling overboard, mechanical failure, grounding, flooding, collision, fire, capsizing, and other accidents. According to FSA, the study followed these steps: (1) Hazard Identification: defining scenarios, (2) Risk Assessment: calculating accident risk levels, (3) Risk Control Options Selection: identifying risk control alternatives, (4) Cost-Benefit Analysis: evaluating the effectiveness of risk control measures, and (5) Recommendations for Decision-Making: proposing strategies to minimize accidents.