ENHANCED AVIATION SAFETY WITH MACHINE LEARNING CRASH PREDICTION

This paper presented a machine learning-based approach for predicting aviation crash risks using historical aviation data. The proposed system analyzes important flight parameters and identifies patterns associated with accident risks. The results demonstrate that machine learning techniques can improve the accuracy of aviation safety predictions and support better decision-making. By predicting potential crash risks in advance, aviation authorities can take preventive actions and enhance overall flight safety. Future work may focus on improving the prediction accuracy by using advanced deep learning models and integrating real-time flight data.

[1] A.B. Arockia Christopher, S. Appavu, “Data Mining Approaches for Aircraft Accidents Prediction”, Emerging Trends in Computing, Communication and Nanotechnology, 2013, Pages 25-26. [2] https://github.com/AeroPython/flight-safety-analysis. [3] http://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx- dataset [4] AshA, g.k, mAnJunATh, A.s. and JAyArAm, m.A. A comparative study of attribute selection using gain ratio and correlation based feature selection, Int J of Info Tech and Knowledge Management, July-December 2012, 2, pp271- 277.  [5] https://github.com/Data4Democracy/crash-model [6] Analysis of Aviation Accident Causality and Severity by H. Li and H. Li (2019): This study uses statistical analysis to identify the causes of aviation accidents and their impact on accident severity. [7] A Comparative Study on Aircraft Accident Severity Prediction Using Data Mining Techniques by M. Hosseinnezhad et al. (2017): This paper compares the performance of different data mining techniques for predicting the severity of aviation accidents. [8] Predicting Aircraft Accident Severity using Random Forest Algorithm by P. V. Jadhav and P. R. Nemade (2017): This study applies the random forest algorithm to aviation accident data to predict the severity of accidents. [9] Predicting Aircraft Accident Severity using Support Vector Machines by P. V. Jadhav and P. R. Nemade (2017): This paper proposes the use of support vector machines to predict the severity of aircraft accidents.  [10] A Comparative Study of Predicting Aircraft Accident Severity by C. Lin et al. (2020): This study compares the performance of different machine learning algorithms for predicting the severity of aircraft accidents.