A Fuzzy TOPSIS Based Model for Safety Risk Assessment of Operational Flight Data
Flight Data Monitoring (FDM) program assists an
operator in aviation industries to identify, quantify, assess and
address operational safety risks, in order to improve safety of flight
operations. FDM is a powerful tool for an aircraft operator integrated
into the operator’s Safety Management System (SMS), allowing to
detect, confirm, and assess safety issues and to check the
effectiveness of corrective actions, associated with human errors.
This article proposes a model for safety risk assessment level of flight
data in a different aspect of event focus based on fuzzy set values. It
permits to evaluate the operational safety level from the point of view
of flight activities. The main advantages of this method are proposed
qualitative safety analysis of flight data. This research applies the
opinions of the aviation experts through a number of questionnaires
Related to flight data in four categories of occurrence that can take
place during an accident or an incident such as: Runway Excursions
(RE), Controlled Flight Into Terrain (CFIT), Mid-Air Collision
(MAC), Loss of Control in Flight (LOC-I). By weighting each one
(by F-TOPSIS) and applying it to the number of risks of the event,
the safety risk of each related events can be obtained.
[1] Wang; Yongjun, Dong; Jiang, Liu; Xiaodong, Zhang; Lixin,”
Identification and standardization of maneuvers based upon operational
flight data”, Chinese Journal of Aeronautics, Volume 28, Issue 1,
February 2015, Pages 133–140.
[2] Annex 6: to the Convention on International Civil Aviation, Part I:
International Commercial Air Transport — Aeroplanes, Ninth Edition:
July 2010.
[3] ICAO: Manual on Flight Data Analysis Programmes (FDAP);
Doc10000; First Edition-2014.
[4] CAAP (Civil Aviation Advisory Publication) SMS-4(0): Guidance on
the establishment of a flight Data Analysis Program (FDAP) – Safety
Management Systems (SMS); August 2011.
[5] Jingru Yan, Jonathan Histon,” Flight Data Monitoring and Human
Factors Risks Identification: A Review of Best Practices”, University of
Waterloo;2015.
[6] CAP 739: Flight Data monitoring, Second Edition: June 2013, ISBN 978
0 11792 840 4.
[7] Liu J, Long G, Xu X. A Method of Multi-Attribute Decision Making
Based On Basic Point and Weighting Coefficients Range. In: Procedia
Computer Science of 7th International Congress of Information and
Communication Technology :107, Sanya, China, 1 Jan - 2 Jan 2017,
paper no.31, pp. 202-205.China: ICICT.
[8] Aliyev RR. Multi-attribute Decision Making Based on Z-valuation. In:
Procedia Computer Science of 12th International Conference on
Application of Fuzzy Systems and Soft Computing: 102, Vienna,
Austria, 29 Aug -30 Aug 2016, paper no.36, pp. 218-222. Austria:
ICAFS.
[9] Erdogan SA, Šaparauskas J, Turskis Z. Decision Making in Construction
Management: AHP and Expert Choice Approach. In: Procedia
Engineering of 12th International Conference on Modern Building
Materials, Structures and Techniques: 172, Vilnius, Lithuania, 26 May-
27 May 2016, paper no.35, pp.270-6. Lithuania: MBMST.
[10] Abdullah L. Fuzzy multi criteria decision making and its applications: a
brief review of category. In: Procedia-Social and Behavioral Sciences of
The 9th International Conference on Cognitive Science: 97, Kuching,
Sarawak, Malaysia, 27Aug-30Aug 2013, paper no.14, pp. 131-136.
Malaysia –ICCS.
[11] Lan J, Jin R, Zheng Z, et al. Priority degrees for hesitant fuzzy sets:
Application to multiple attribute decision making. J Operations Research
Perspectives 2017; 4(1):67-73.
[12] Chattopadhyay S. A neuro-fuzzy approach for the diagnosis of
depression. J Applied Computing and Informatics 2014; 13(1):10-18.
[13] N˘ad˘abana S, Dzitacb S, Dzitaca I. Fuzzy TOPSIS: A General View.
Information Technology and Quantitative Management (ITQM 2016):
Procedia Computer Science 91 (2016) 823 – 831.
[14] Javad Seyedmohammadia, Foredoom S, Jafarzadeha A, Ghorbanic M,
Shahbazia F.Application of SAW, TOPSIS and fuzzy TOPSIS models in
cultivation priority planning for maize, rapeseed and soybean crops.J
Geoderma 2018;310: 178–190.
[15] Wanga L, Rena Y, Wub C. Effects of flare operation on landing safety:
A study based on ANOVA of real flight data. Safety Science 102 (2018)
14–25.
[16] Walker G. Redefining the incidents to learn from: Safety science
insights acquired on the journey from black boxes to Flight Data
Monitoring. J Safety Science 2017 ;99: 14–22.
[17] ICAO (International Civil Aviation Organization): Safety Management
Manual (SMM); Doc 9859AN/474; Third Edition — 2013.
[18] Developing Standardised FDM-Based Indicators | Focus on operational
risks identified in the European Plan for Aviation Safety Version 2
(December 2016)).
[1] Wang; Yongjun, Dong; Jiang, Liu; Xiaodong, Zhang; Lixin,”
Identification and standardization of maneuvers based upon operational
flight data”, Chinese Journal of Aeronautics, Volume 28, Issue 1,
February 2015, Pages 133–140.
[2] Annex 6: to the Convention on International Civil Aviation, Part I:
International Commercial Air Transport — Aeroplanes, Ninth Edition:
July 2010.
[3] ICAO: Manual on Flight Data Analysis Programmes (FDAP);
Doc10000; First Edition-2014.
[4] CAAP (Civil Aviation Advisory Publication) SMS-4(0): Guidance on
the establishment of a flight Data Analysis Program (FDAP) – Safety
Management Systems (SMS); August 2011.
[5] Jingru Yan, Jonathan Histon,” Flight Data Monitoring and Human
Factors Risks Identification: A Review of Best Practices”, University of
Waterloo;2015.
[6] CAP 739: Flight Data monitoring, Second Edition: June 2013, ISBN 978
0 11792 840 4.
[7] Liu J, Long G, Xu X. A Method of Multi-Attribute Decision Making
Based On Basic Point and Weighting Coefficients Range. In: Procedia
Computer Science of 7th International Congress of Information and
Communication Technology :107, Sanya, China, 1 Jan - 2 Jan 2017,
paper no.31, pp. 202-205.China: ICICT.
[8] Aliyev RR. Multi-attribute Decision Making Based on Z-valuation. In:
Procedia Computer Science of 12th International Conference on
Application of Fuzzy Systems and Soft Computing: 102, Vienna,
Austria, 29 Aug -30 Aug 2016, paper no.36, pp. 218-222. Austria:
ICAFS.
[9] Erdogan SA, Šaparauskas J, Turskis Z. Decision Making in Construction
Management: AHP and Expert Choice Approach. In: Procedia
Engineering of 12th International Conference on Modern Building
Materials, Structures and Techniques: 172, Vilnius, Lithuania, 26 May-
27 May 2016, paper no.35, pp.270-6. Lithuania: MBMST.
[10] Abdullah L. Fuzzy multi criteria decision making and its applications: a
brief review of category. In: Procedia-Social and Behavioral Sciences of
The 9th International Conference on Cognitive Science: 97, Kuching,
Sarawak, Malaysia, 27Aug-30Aug 2013, paper no.14, pp. 131-136.
Malaysia –ICCS.
[11] Lan J, Jin R, Zheng Z, et al. Priority degrees for hesitant fuzzy sets:
Application to multiple attribute decision making. J Operations Research
Perspectives 2017; 4(1):67-73.
[12] Chattopadhyay S. A neuro-fuzzy approach for the diagnosis of
depression. J Applied Computing and Informatics 2014; 13(1):10-18.
[13] N˘ad˘abana S, Dzitacb S, Dzitaca I. Fuzzy TOPSIS: A General View.
Information Technology and Quantitative Management (ITQM 2016):
Procedia Computer Science 91 (2016) 823 – 831.
[14] Javad Seyedmohammadia, Foredoom S, Jafarzadeha A, Ghorbanic M,
Shahbazia F.Application of SAW, TOPSIS and fuzzy TOPSIS models in
cultivation priority planning for maize, rapeseed and soybean crops.J
Geoderma 2018;310: 178–190.
[15] Wanga L, Rena Y, Wub C. Effects of flare operation on landing safety:
A study based on ANOVA of real flight data. Safety Science 102 (2018)
14–25.
[16] Walker G. Redefining the incidents to learn from: Safety science
insights acquired on the journey from black boxes to Flight Data
Monitoring. J Safety Science 2017 ;99: 14–22.
[17] ICAO (International Civil Aviation Organization): Safety Management
Manual (SMM); Doc 9859AN/474; Third Edition — 2013.
[18] Developing Standardised FDM-Based Indicators | Focus on operational
risks identified in the European Plan for Aviation Safety Version 2
(December 2016)).
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:78245", author = "N. Borjalilu and P. Rabiei and A. Enjoo", title = "A Fuzzy TOPSIS Based Model for Safety Risk Assessment of Operational Flight Data", abstract = "Flight Data Monitoring (FDM) program assists an
operator in aviation industries to identify, quantify, assess and
address operational safety risks, in order to improve safety of flight
operations. FDM is a powerful tool for an aircraft operator integrated
into the operator’s Safety Management System (SMS), allowing to
detect, confirm, and assess safety issues and to check the
effectiveness of corrective actions, associated with human errors.
This article proposes a model for safety risk assessment level of flight
data in a different aspect of event focus based on fuzzy set values. It
permits to evaluate the operational safety level from the point of view
of flight activities. The main advantages of this method are proposed
qualitative safety analysis of flight data. This research applies the
opinions of the aviation experts through a number of questionnaires
Related to flight data in four categories of occurrence that can take
place during an accident or an incident such as: Runway Excursions
(RE), Controlled Flight Into Terrain (CFIT), Mid-Air Collision
(MAC), Loss of Control in Flight (LOC-I). By weighting each one
(by F-TOPSIS) and applying it to the number of risks of the event,
the safety risk of each related events can be obtained.", keywords = "F-TOPSIS, fuzzy set, FDM, flight safety.", volume = "12", number = "12", pages = "1073-8", }
