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  4. Vol 7, Issue 6, 2023

Research on flight technology evaluation based on machine learning algorithm

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DOI: 10.23977/acss.2023.070616 | Downloads: 13 | Views: 480

Author(s)

Kun Tang 1, Weijie Wang 1, Zhendong Guo 1, Junjie Liang 2, Kaipeng Yuan 3, Liuqing Huang 4

Affiliation(s)

1 School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, China
2 School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, China
3 Binhai Agricultural College, Guangdong Ocean University, Zhanjiang, China
4 College of Food Science and Technology, Guangdong Ocean University, Zhanjiang, China

Corresponding Author

Zhendong Guo

ABSTRACT

In China's civil aviation transportation industry, flight safety has been the focus of attention. In this paper, a flight technology assessment model and an automated early warning model are established for aviation safety. First, data pre-processing is performed. Then the suitable indicators are continuously screened by multiple machine learning classifications, and then the screened data are fitted to continuously screen the suitable indicators, and the aircraft technology assessment is found to be more suitable for the integrated learning classification model. Subsequently, three unoptimized optimal models were derived as LightGBM, XGboost and Random Forest classification models. The results of these models are then fused by Stacking model to combine their advantages to build the final aircraft technology assessment prediction model. For the automated early warning mechanism, the aviation early warning mechanism needs to be established first by subclassing these data with the K-mean clustering model and visualizing the key data items such as avg (COG NORM ACCEL) based on the normal distribution, combined with the differentiated distribution for each category to set the implausible warning level to establish the aviation automated early warning model.

KEYWORDS

Random forest classification, stacking model fusion, machine learning, aviation security

CITE THIS PAPER

Kun Tang, Weijie Wang, Zhendong Guo, Junjie Liang, Kaipeng Yuan, Liuqing Huang, Research on flight technology evaluation based on machine learning algorithm. Advances in Computer, Signals and Systems (2023) Vol. 7: 128-135. DOI: http://dx.doi.org/10.23977/acss.2023.070616.

REFERENCES

[1] Xie Jiayi. Research on Unstable Approach Risk Analysis and Early Warning Technology Based on QAR Flight Big Data [D]. Wuhan University, 2021.
[2] Ma Li. Optimization and improvement of random forest algorithm [D]. Jinan University, 2016.
[3] Shu Shiwen. LightGBM model and its application [J]. Information Record Materials, 2022, 23(07): 219-222.
[4] Sun Ruishan; Xiao Yabing. Research on the structure of civil aviation pilot operation characteristics index based on QAR record data [J]. China Safety Production Science and Technology, 2012(11)
[5] Shi Jiaqi, Zhang Jianhua. Load forecasting method based on multi-model fusion Stacking integrated learning approach [J]. Chinese Journal of Electrical Engineering, 2019, 39(14):4032-4042.

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