Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach

Abstract

Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.