# An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data
## 5 Conclusions  
Flight safety plays a vital role in the aviation industry. In this paper, we addressed this issue by investigating pilots' inappropriate stick operation during the flare phase. Specifically, we extracted key features from flight parameters with expert knowledge and took advantage of the K-means clustering algorithm to uncover the reasons for this adverse event. Based on the clustering results, we summarized the reasons into four types, including the headwind influence, high pitch influence, height influence, and pilot personal influence. In addition, we further analyzed the characteristics of the four types of reasons from different airlines, airports, and pilots. The results in this paper can provide researchers with new insights into this problem.  
This study is not immune from its limitations. Firstly, our datasets were only for two different airlines. In the future, we will try to incorporate more airlines in our research. Secondly, the methodology in this paper is based on K-means clustering, which has its own limitations. In the future, we will investigate new clustering algorithms to improve the method performance. Lastly, in this paper, we only conducted a data-level analysis with limited domain knowledge. In the future, we will conduct more in-depth research and incorporate more expert knowledge to increase the generality of our results.  
Another important direction worth investigating in the future is applying advanced optimization algorithms to address the flight safety problem, such as the online-learning-based evolutionary many-objective algorithm [41], polyploid memetic algorithm [42], island-based metaheuristic algorithm [43], etc. These algorithms, which are mainly based on heuristics or metaheuristics, have been widely used for challenging decision problems in a variety of domains, such as vehicle routing [44], berth scheduling [43; 45], ambulance routing in a disaster response [46], etc.  
Conceptualization, X.L. and Y.Q.; methodology, J.S.; software, H.C.; validation, L.Z. and Q.W.; formal analysis, J.S.; investigation, H.C.; resources, X.L., Y.Q. and L.Z.; data curation, J.S.; writing--original draft preparation, H.C.; writing--review and editing, J.S.; supervision, X.L. and L.Z.; project administration, Y.Q.; funding acquisition, X.L. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. U2133209), the Civil Aviation Flight Technology and Flight Safety Key Laboratory Foundation (No. FZ202 ZZ01), and the Open Fund of the Key Laboratory of Flight Techniques and Flight Safety, CAAC (No. FZ021 KF01), and the APC was funded by the National Natural Science Foundation of China (No. U2133209).  
Not applicable.  
Figure 9: Distributions of four types of adverse events for different pilots.  
**Acknowledgments:** We appreciate [PERSON] and [PERSON], [PERSON], and [PERSON] for their support in the constructive discussions and experience-based feature extraction work.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.