# Classification and Causes Identification of Chinese Civil Aviation Incident Reports
## 5 Conclusions  
In this study, we explored a method for processing civil aviation accident reports that can be applied in practice. Using this method, we achieve the automatic classification and cause identification of Chinese civil aviation incident reports from the CASTC database. The Python language and several Python libraries were used in implementing these methods. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1**} \\ \hline  & **RAW** & **Word2 Vec** & **RAW** & **Word2 Vec** & **RAW** & **Word2 Vec** \\ \hline EI1 & 0.938 & 0.682 & 1.000 & 1.000 & 0.968 & 0.811 \\ EI2 & 0.800 & 0.765 & 0.900 & 0.975 & 0.847 & 0.857 \\ EI3 & 1.000 & 0.833 & 1.000 & 1.000 & 1.000  
First, the XGBoost classifier and OC-POS vectorization methods are used to classify the text reports, as these methods perform best among ten classifiers and three vectorization strategies. As a result, the overall F1 score was 0.85, the precision for different categories was 0.50 to 0.99, the recall was 0.50 to 1.00, and the F1 score was 0.50 to 0.99, which is better than the results of other studies. This method enabled the automatic classification of reports, and the need to analyze several kinds of incidents could be satisfied. Second, we built a rule-based system to identify the causes of incidents. The proposed system obtained an F1-score above 0.90 when identifying 25 causes (8, 7, 7, and 3 equipment, human, environment, and organization causes, respectively) from our dataset. In addition, the basic rules were extended using Word2 Vec. However, the precision improved, and the overall results (F1-score) worsened. Unlike most studies, our study was conducted on a Chinese dataset. It makes this study different in the pre-processing steps and text analysis. There are some limitations of this study. First, the quality of the reports may influence the results. The database contains reports from different airlines, spanning more than a decade, and with inconsistent writing norms and standards. Invalid information might be recorded and could interfere with the experiment. In addition, writing errors (e.g., incorrect wording or missing and irregular punctuation) may occur during recording, transcription, and decoding. Although we corrected some mistakes before the experiment, the remaining errors may still compromise the robustness of cause identification. Second, these methods rely on the stability of libraries. In the classification, Jieba was adopted to process the Chinese tokenization, and the results were used to represent the reports. Third, the support samples were limited. Since the report writing is complex and conducting manual analysis is time-consuming and labor-intensive, we only selected a random portion of reports from the database for testing in the experiment. Finally, the identification of causes is not fully automatic. It still requires the manual collection of rules as well as keywords. Further studies can be extended on this work. For classification, though the word-embedding model does not perform well in the experiments in this paper, they can try other word-embedding models. In addition, word-embedding models and TF-IDF vectorization cannot capture important information from long texts, which can be addressed by testing deep-learning methods with attention mechanisms. Furthermore, in this study, we obtained the causes and categories from the incident reports, which made it possible to analyze them further. Due to the complexity of incident reporting, especially in Chinese, a rule-based approach coupled with human intervention seems be a powerful approach to explore. In addition to causes, other crucial information can be identified in reports, such as the consequences of incidents. Conceptualization, Y.J. and J.D. ; methodology, Y.J. ; software, J.D. ; validation, Y.J., J.H. and H.S. ; formal analysis, J.D. ; resources, Y.J. ; data curation, Y.J. and J.D. ; writing--original draft preparation, J.D.