Classification of Aviation Accidents Using Data Mining Algorithms

Yıl 2022, Cilt: 10 Sayı: 1, 10 - 15, 30.01.2022

Emre Kuşkapan , Mohammad Ali Sahraei Muhammed Yasin Çodur

https://doi.org/10.17694/bajece.793368

Öz

Air transportation is a very preferred type of transportation for long-distance trips worldwide. This type of transportation has made great progress with the development of technology. In addition to its technological developments, passenger capacity is gradually increasing due to its fast and secure access. In contrast, the mortality rate is quite high in the case of an airplane accident, and hundreds of people die in a single accident. This research aims to classification several airplane accidents to find crucial factors and their overall impacts on the mentioned accident. In this study, appropriate data associated with said accidents worldwide since 2000 have been collected and then analyzed using sequential minimal optimization, decision tree (J48), and Naive Bayes. It is revealed that the decision tree algorithm provided the most accurate results for the study. Finally, appropriate comments were elaborated about each stage to reduce accidents. If these evaluations are taken into account, air transport will be much more reliable and thus loss of life will be minimized.

Anahtar Kelimeler

Aviation safety, sequential minimal optimization, decision tree, Naive Bayes

Kaynakça

• [1] M. Terzioğlu, Human errors as a cause of aviation accidents can be reduced through crew resource management, Master thesis, Department of Human Resources Dokuz Eylul University, İzmir, Turkey, 2007.
• [2] K. Dönmez, S. Uslu, "A Study on communication induced accidents and incidents in aviation." The Journal of International Social Research, vol. 45, 2016, pp. 1074-1079.
• [3] S. Metin, "Human Factors in Aviation Accidents in Last Years. "1. National Aviation Medicine Congress," pp. 22–24, Eskişehir, Turkey, 2014.
• [4] H. Kharoufah, J. Murray, G. Baxter, G. Wild, "A review of human factors causations in commercial air transport accidents and incidents: From to 2000–2016." Progress in Aerospace Sciences, vol. 99, 2018, pp. 1-13.
• [5] V. Andersen, T. Bove, "A feasibility study of the use of incidents and accident reports to evaluate effects of team resource management in air traffic control. " Safety Science, vol. 35(1-3), 2000, pp. 87-94.
• [6] V. Socha, L. Socha, V. Němec, "Air accidents, their investigation and prevention. " eXclusive e-Journal, vol. 4, 2014, pp. 1-9.
• [7] M. Bazargan, V. S. Guzhva, "Impact of gender, age and experience of pilots on general aviation accidents." Accident Analysis & Prevention, vol. 43(3), 2011, pp. 962-970.
• [8] W. C. Moon, K. E. Yoo, Y. C. Choi, "Air traffic volume and air traffic control human errors." Journal of Transportation Technologies, vol. 1(03), 47, 2011.
• [9] C. V. Oster, J.S. Strong, C. Kurt Zorn, "Investigation of accidents related to air traffic control." 51st Annual Transportation Research Forum 2010, pp. 853–872, United States, 2010.
• [10] S. Uslu, K. Dönmez, "Investigation of accidents related to air traffic control," Mehmet Akif Ersoy University Journal of Social Sciences Institute, vol. 8, 2017, pp: 271–287.
• [11] S. Z. Liang Cheng, R. M. Arnaldo Valdés, V. F. Gómez Comendador, F. J. Sáez Nieto, "Detection of Common Causes between Air Traffic Serious and Major Incidents in Applying the Convolution Operator to Heinrich Pyramid Theory." Entropy, vol. 21(12), 2019, 1166.
• [12] W. Kaleta, J. Skorupski, "A fuzzy inference approach to the analysis of LPV-200 procedures influence on air traffic safety." Transportation Research Part C: Emerging Technologies, vol. 106, 2019, pp. 264-280.
• [13] G. W. Van Es, "A Review of Civil Aviation Accidents Air Traffic Management Related Accidents: 1980-1999." In 4th International Air Traffic Management R&D Seminar New-Mexico, 2001.
• [14] W. K. Lee, "Risk assessment modeling in aviation safety management." Journal of Air Transport Management, vol. 12(5), 2006, pp. 267-273.
• [15] M. Lower, J. Magott, J. Skorupski, "Analysis of Air Traffic Incidents using event trees with fuzzy probabilities." Fuzzy sets and systems, vol. 293, 2016, pp. 50-79. [16] Z. H. Ming, Q. L. Zhong, M. Zhang, S. C. Han, "New analytic calculating method for Performance-Based Navigation airway capacity." Advances in Transportation Studies, vol. 1, 2015, pp. 85-90.
• [17] S. H. Stroeve, H. A. Blom, G. B. Bakker, "Systemic accident risk assessment in air traffic by Monte Carlo simulation." Safety Science, vol. 47(2), 2009, pp. 238-249.
• [18] P. Brooker, "Air Traffic Management accident risk. Part 1: The limits of realistic modelling." Safety science, vol. 44(5), 2006, pp. 419-450.
• [19] H. İncekaş, Increasing Flight Safety, and Preventing Accident Crimes Android Based Checklist, Master Thesis, Department of Computer Engineering, İzmir Katip Çelebi University, İzmir, Turkey, 2017.
• [20] "Number of flights performed by the global airline industry from 2004 to 2020." https://www.statista.com/statistics/564769/airline-industry-number-of-flights/, accessed 25 June 2020
• [21] "Airline accident statistics. " https://aviation-safety.net/statistics/period/ stats.php, accessed 25 June 2020
• [22] "Waikato Environment for Knowledge Analysis (WEKA), " https://tr. wikipedia.org/wiki/Weka, accessed 18 June 2020
• [23] L. Yuh-Jye, Sequential minimal optimization. Data Science and Machine Intelligence Lab National Chiao Tung University, 2017.
• [24] "Sequential minimal optimization." https://en.wikipedia.org/wiki/Seq uential _minimal_optimization, accessed 18 June 2020
• [25] K. Atmaca. "Naive Bayesian algorithm." https://kenanatmaca .com/naive-bayesian-algoritmasi/, accessed 3 June 2020
• [26] T. Pala, A.Y. Camurcu, "Design of Decision Support System in the Metastatic Colorectal Cancer Data Set and Its Application." Balkan Journal of Electrical and Computer Engineering, vol. 4(1), 2016, pp. 12-16.
• [27] G.S. Eraldemir, M.T. Arslan, E. Yildirim, "Comparison of random forest and j48 decision tree." Classifiers Using HHT Based Features in EEG, 2017, pp: 1250–1256. [28] A. M. Hormann, "Programs for machine learning." Part II. Information and Control, vol. 7(1), 1964, pp. 55-77.
• [29] K. R. Pradeep, N. C. Naveen, "Predictive analysis of diabetes using J48 algorithm of classification techniques." In 2016 2nd International Conference on Contemporary Computing and Informatics. pp. 347-352, 2016.
• [30] S. Aljawarneh, M. B. Yassein, M. Aljundi, "An enhanced J48 classification algorithm for the anomaly intrusion detection systems." Cluster Computing, vol. 22(5), 2019, pp.10549-10565.
• [31] T. C. Smith, E. Frank, "Introducing machine learning concepts with WEKA." In Statistical genomics. pp. 353-378. Humana Press, New York, Usa, 2016.
• [32] R. R. Bouckaert, E. Frank, M. A. Hall, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten, WEKA---Experiences with a Java Open-Source Project. The Journal of Machine Learning Research, 11, 2010, pp. 2533-2541.
• [33] Z. H. Zhou, "Learnware: on the future of machine learning." Frontiers Comput. Sci., vol. 10(4), 2016, pp. 589-590.
• [34] G. Kayhan, E. Ergün, "Medicinal and Aromatic Plants Identification Using Machine Learning Methods". Balkan Journal of Electrical and Computer Engineering, vol. 8(1), 2020, pp. 81-87.
• [35] "What is Root Mean Square Error (RMSE)?" https://www.statistic showto.com/probability-and-statistics/regression-analysis/rmse-rootme an-square-error/, accessed 11 June 2020
• [36] A. Saxena, M. K. Jat, "Analysing performance of SLEUTH model calibration using brute force and genetic algorithm–based methods." Geocarto International, vol. 35(3), 2020, pp. 256-279.
• [37] M. Zhang, "Intelligent route scheduling method for non-full-loaded vehicle based on unbalanced data mining." Advances in Transportation Studies, 1, 2019, pp. 73-83.
• [38] C. Sammut, G.I. Webb, Mean Absolute Error, Encyclopedia of Machine Learning and Data Mining, Springer US, Boston, MA, p. 652. 2010.