# Comparative Study of Deep Learning Architectures for Textual Damage Level Classification

arXiv: https://arxiv.org/abs/2501.01694 · HTML: https://mlsanity.com/paper/2501.01694/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 3 Jan 2025.

## Builds on (most cited first, 1 of 1)

- [A Basic Recurrent Neural Network Model](https://mlsanity.com/paper/1612.09022/) (2016)

## Built on by (most cited first, 2 of 2)

- [Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences](https://mlsanity.com/paper/2501.06564/) (2025)
- [Utilizing AI for Aviation Post-Accident Analysis Classification](https://mlsanity.com/paper/2506.00169/) (2025)

## Abstract (arXiv)

Given the paramount importance of safety in the aviation industry, even minor operational anomalies can have significant consequences. Comprehensive documentation of incidents and accidents serves to identify root causes and propose safety measures. However, the unstructured nature of incident event narratives poses a challenge for computer systems to interpret. Our study aimed to leverage Natural Language Processing (NLP) and deep learning models to analyze these narratives and classify the aircraft damage level incurred during safety occurrences. Through the implementation of LSTM, BLSTM, GRU, and sRNN deep learning models, our research yielded promising results, with all models showcasing competitive performance, achieving an accuracy of over 88% significantly surpassing the 25% random guess threshold for a four-class classification problem. Notably, the sRNN model emerged as the top performer in terms of recall and accuracy, boasting a remarkable 89%. These findings underscore the potential of NLP and deep learning models in extracting actionable insights from unstructured text narratives, particularly in evaluating the extent of aircraft damage within the realm of aviation safety occurrences.