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Controlled flight into terrain (CFIT) is considered a typical accident category of “low-probability-high consequence”. Human factors play an important role in CFIT accidents in such a complex and high-risk system. This study aims to explore the causal relationship and inherent correlation of CFIT accidents by the Human Factors Analysis and Classification System (HFACS) and Bayesian network (BN). A total of 74 global CFIT accident investigation reports from 2001 to 2020 were collected, and the main contributing factors were classified and analyzed based on the Human Factors Analysis and Classification System. Then, the model was transformed into a Bayesian network topology structure. To ensure accuracy, the prior probability of each root node was computed by the fuzzy number theory. Afterward, using the bidirectional reasoning ability of the Bayesian network under uncertainty, this study performed a systematic quantitative analysis of the controlled flight into terrain accidents, including causal reasoning analysis, diagnostic analysis, sensitivity analysis, most probable explanation, and scenario analysis. The results demonstrate that the precondition for unsafe acts (30.5%) has the greatest impact on the controlled flight into terrain accidents among the four levels of contributing factors. Inadequate supervision, intentional noncompliance with SOPs/cross-check, GPWS not installed or failure, adverse meteorological environment, and ground-based navigation aid malfunction or not being available are recognized as the top significant contributing factors. The contributing factors of the high sensitivity and most likely failure are identified, and the coupling effect between the different contributing factors is verified. This study can provide guidance for CFIT accident analysis and prevention.

Generally, airplane upsets in flight are considered a precursor to loss of control in flight (LOC-I) accidents, and unfortunately LOC-I is classified as the leading cause of fatal accidents. To further explore the risk factors, causal relationships, and coupling mechanism of airplane upsets, this study proposed a risk analysis model integrating the Interpretative Structural Modeling (ISM) and Bayesian Network (BN). Seventeen key risk factors leading to airplane upsets were identified through the analysis of typical accident cases and the literature. The ISM approach was used to construct the multi-level interpretative structural model of airplane upsets, which could reveal the causal relationship among various risk factors and risk propagation paths. Then, taking 286 accident/incident investigation data as training samples, a data-driven BN model was established using machine learning for dependency intensity assessment and inference analysis. The results reveal that the interaction among risk factors of fatal accidents caused by airplane upsets is more significant than that of non-fatal accidents/incidents. Risk factors such as pilot-induced oscillations/airplane-pilot coupling and non-adherence to Standard Operating Procedures (SOPs)/neglect of cross-validation have a significant effect on airplane upsets in flight among seventeen risk factors. Moreover, this study also identifies the most likely set of risk factors that lead to fatal accidents caused by airplane upsets. The research results have an important theoretical significance and application value for preventing airplane upsets risk.