Semantic Causal-Factor Inference from Aviation Incident Narratives Using A Variational Autoencoder with Cosine-Similarity-Based Reconstruction
Authors: AZIIDA NANYONGA1, HASSAN WASSWA, UGUR TURHAN, KEITH FRANCIS JOINER, GRAHAM WILD
Organizations: SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia · School of Systems and Computing, University of New South Wales, Canberra, ACT 2600, Australia · School of Science, University of New South Wales, Canberra, ACT 2612, Australia · Capability Systems Centre, University of New South Wales, Canberra, ACT 2600, Australia
Aviation accident and incident investigations generate extensive unstructured textual information containing evidence relevant to the causes and contributing factors of safety occurrences. Automatically extracting such information is challenging because causal evidence may be distributed across long and complex investigation narratives. This study proposes a semantic causal-factor inference framework combining natural language processing with a variational autoencoder (VAE) to learn the relationship between aviation investigation narratives and expert-reported probable causes. Investigation narratives and their corresponding probable causes are transformed into numerical representations, after which the encoder maps narrative representations to a probabilistic latent space. The decoder estimates representations of the corresponding probable causes and is trained using an objective that combines Kullback-Leibler divergence with cosine-similarity-based semantic reconstruction. The framework was evaluated using 20,919 finalized U.S. National Transportation Safety Board investigation reports from 2005 to 2020. On the held-out test set, the predicted and expert-reported probable-cause representations achieved a mean cosine similarity of 0.786 (SD = 0.120). The predicted representations also yielded interpretable terms associated with causal information in the reports. The results demonstrate the potential of probabilistic latent representation learning for AI-assisted extraction of causal information from aviation safety narratives while retaining expert investigation as the basis for formal causal determination.
Figures & tables
Figure 1. Proposed framework for semantic causal-factor inference from aviation investigation narratives. The investigation narrative is preprocessed and numerically represented before being encoded into a latent representation. The decoder estimates the representation of the corresponding probable cause, and semantic similarity between the predicted and expert-reported representations is incorporated into model training.
Characteristic
Value
Data source
U.S. National Transportation Safety Board (NTSB)
Investigation status
Finalized reports
Study period
2005–2020
Number of records
20,919
Model input
Analysis narrative
Model target
Probable cause
Table 1. Characteristics of the NTSB aviation investigation dataset used in this study.
Encoder
Decoder
Layer
Filters/Units
Output Shape
Activation
Kernel Size
Parameters
Layer
Filters/Units
Output Shape
Activation
Kernel Size
Parameters
Input
–
(1200,1)
–
–
0
Input
20
(20,)
–
–
0
–
–
–
–
–
0
Dense
64
(64,)
LeakyReLU
–
1,344
–
–
–
–
–
0
Reshape
–
(1,64)
–
–
0
Conv1D
256
(134,256)
LeakyReLU
36
9,472
Conv1DTranspose
64
(3,64)
LeakyReLU
4
16,448
Conv1D
128
(17,128)
LeakyReLU
25
819,328
Conv1DTranspose
64
(6,64)
LeakyReLU
4
16,448
Table 2. Final VAE encoder and decoder architecture used in the experiments, including the number of trainable parameters in each layer.
Figure 2. Inference procedure for estimating causal information from an aviation investigation narrative. The narrative sequence si is converted into a numerical representation vi and processed by the encoder to obtain the latent representation zi . The decoder produces the predicted causal representation v^i , which is subsequently mapped back to the textual representation space to obtain interpretable causal terms s^i .
Optimizer
Learning Rate
Mean Cosine Similarity
SD
Adam
0.0100
0.623
0.126
0.0010
0.663
0.150
0.0001
0.644
0.163
Nadam
0.0100
0.663
0.156
0.0010
0.661
0.140
0.0001
0.644
0.158
Table 3. Optimizer and learning-rate evaluation during model development.
Figure 3. Distribution of cosine-similarity scores between the predicted causal representations and the corresponding expert-reported probable-cause representations for the held-out test observations.
Figure 4. Cosine-similarity scores obtained across individual observations in the held-out test subset.
Figure 5. Word-cloud visualizations of terms recovered from predicted causal representations for four selected test observations. The examples contain terms associated with (a) operational, maintenance, and environmental factors; (b) engine and mechanical components; (c) flight dynamics and landing-related conditions; and (d) inspection, aircraft-component, and damage-related information. The word clouds are provided as qualitative illustrations of the information represented by the model outputs rather than as quantitative measures of causal importance.
Figure 6. Example of the original analysis narrative and corresponding probable-cause information extracted from an NTSB investigation record before text preprocessing.
Figure 7. Processed textual representations of the analysis narrative and probable cause for the example investigation record after tokenization and removal of stop words and punctuation.