## 4 Results II--Review of Research Clusters
### Cluster 1: Risk/Accident Analysis
#### 4.2.1 Sub-Cluster 1: Accident Analysis
FSA offers a systematic and comprehensive approach but is highly dependent on data accuracy and requires significant time and resources. To address these limitations, the study recommended simplified FSA procedures or approaches that reduce uncertainties associated with human error. Research on analyzing accidents using Bayesian Networks (BN) is on the rise. [PERSON] et al. (2020) [20] analyzed accidents using BN modeling, followed by Chi-square independence testing. The BN approach constructs a hierarchical network that models accident causes, environmental factors, and potential risks, allowing for a precise analysis of the conditional probability of accident occurrence. The Chi-square test was then applied to examine the statistical relationship between accident types and various factors, such as vessel type, vessel length, vessel age, and operation conditions. This comprehensive approach enabled the study to determine the main causes and severity levels of fishing vessel accidents, particularly critical factors influencing capsizing and collision incidents. Despite its strengths, the study suggested incorporating automation processes to enhance modeling efficiency. [PERSON] et al. (2023) [58] quantitatively categorized accidents and identified major risk factors using BN and FSA methodologies. They found that the most common accident types were slips/falls, collisions, and entrapments, and highlighted that implementing safety checklists (CL) and protective equipment (PE) played a crucial role in reducing accident rates. While these methods allowed for probabilistic analysis and accurate identification of accident causes, they do not account for causal relationships between variables, making real-world application challenging. Therefore, causal inference techniques and sensitivity analysis were recommended to improve the interpretation of variable relationships. Additionally, in recent years, there has been an increasing trend of integrating various analytical methods to complement the previously mentioned approaches. [PERSON] et al. (2023) [3] employed the Random Forest (RF) method and a BN model to analyze fishing vessel accidents, identify Risk Influential Factors (RIF), and establish a predictive model for assessing accident risk. Using the Tree-Augmented Naive Bayes (TAN) approach, which considers dependencies between variables, they built a network to represent interrelationships among variables. Sensitivity analysis indicated that power, ship length, gross tonnage, and operational type were the most influential factors. By employing RF, BN, and TAN methods, the study complemented the shortcomings of each model, demonstrating an effective approach for analyzing large datasets. [PERSON] et al. (2024) [59] applied Fault Tree Analysis (FTA) to model collision accidents and systematically analyzed accident causes. To classify human errors, they utilized the Skill-Rule-Knowledge (SRK) and Slip-Lapse-Mistake-Violation (SLMV) models to analyze human errors in collisions and occupational accidents. While these models are useful for systematically understanding accident causes and decision-making patterns, they have limitations in addressing interactions between human and environmental factors. Thus, strategies for collecting precise and diverse data are needed, and combining FTA with BN could provide a more comprehensive approach to analyzing mutual influences between variables.