Global Cabin Safety Incident Reports from 2004–2024

8,280 global cabin abnormal event reports from 2004–2024 were used to construct a hybrid CNN-LSTM-Attention model for intelligent risk assessment. The model achieved 95.01% accuracy and an F1 score of 94.17% on the test set. The dataset, authored by Lianbin Zhou and shared on figshare, provides a foundation for data-driven decision-making in civil aviation safety management.

Use Cases

• Classifying cabin safety incidents based on textual event reports.
• Training models for intelligent risk assessment in aviation safety.
• Establishing a mapping system from text features to hazard levels for interpretable risk quantification.
• Benchmarking performance of text classification models like XGBoost against hybrid deep learning architectures.

Strengths

• Includes 8,280 global incident reports spanning 20 years (2004–2024).
• Model performance is benchmarked with specific metrics (95.01% accuracy, 94.17% F1 score).
• Dataset is shared under an open CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

The description suggests the data consists of global cabin abnormal event reports, likely gathered from aviation safety authorities.

Time Range

2004–2024

Freshness

Last updated 2026-05-27 17:39:58; freshness should be verified.

Geography

Global