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An aviation accidents prediction method based on MTCNN and Bayesian optimization

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Abstract

The safety of the civil aviation system has been of increasing concern with several accidents in recent years. It is urgent to put forward a precise accident prediction model, which can systematically analyze safety from the perspective of accident mechanism to enhance training accuracy. Furthermore, the predictive model is critical for stakeholders to identify risk and implement the proactive safety paradigm. In this work, to mitigate casualties and economic losses arising from aviation accidents and improve system safety, the focus is on predicting the aircraft damage severity, the injury/death severity, and the flight phases in the sequence of identifying event risk sources. This work establishes a multi-task deep convolutional neural network (MTCNN) learning framework to accomplish this goal. An innovative prediction rule will be developed to refine prediction results from two approaches: handling imbalanced classes and Bayesian optimization. By comparing the performance of the proposed multi-task model with other single-task machine learning models with ten-fold cross-validation and statistical testing, the effectiveness of the developed model in predicting aviation accident severity and flight phase is demonstrated.

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Acknowledgements

The authors gratefully acknowledge the funding support from the National Natural Science Foundation (72271123) and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX23_0393).

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Authors and Affiliations

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Minglan Xiong, Zhaoguo Hou & Huawei Wang

  2. College of Automotive and Transportation Engineering, Nanjing Forestry University, Nanjing, 210037, China

    Changchang Che

  3. Nanjing Chenguang Group Co., Ltd, Nanjing, 210006, China

    Rui Luo

Authors
  1. Minglan Xiong
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  2. Zhaoguo Hou
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Contributions

Minglan Xiong: Writing—original draft, Conceptualization, Methodology, Formal analysis, Data curation, Visualization. Zhaoguo Hou: Writing—review & editing, Conceptualization, Methodology, Software, Formal analysis, Visualization. Huawei Wang: Writing—review & editing, Conceptualization, Supervision, Funding acquisition. Changchang Che & Rui Luo: Writing—review & editing, Data curation, Visualization.

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Correspondence to Huawei Wang.

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Xiong, M., Hou, Z., Wang, H. et al. An aviation accidents prediction method based on MTCNN and Bayesian optimization. Knowl Inf Syst 66, 6079–6100 (2024). https://doi.org/10.1007/s10115-024-02168-6

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