Design of Models and Algorithms for Decision Support in Aviation Safety Tasks
DOI:
https://doi.org/10.1590/jatm.v18.1451Keywords:
Decision support systems, Flight safety, Risk assessment, Machine learning, Real time operation, Belief networksAbstract
The aim of the study was to analyze effective models and algorithms of decision support that contribute to enhancing aviation safety. The study employed decision-making task formalization, probabilistic modeling, machine learning, multi-criteria analysis, and data analysis methods for processing heterogeneous information, including technical parameters, video streams, and behavioral characteristics. The research identified key features of decision support models and algorithms that improve aviation safety through real-time monitoring and adaptive response. Machine learning methods achieved up to 92% accuracy with a response time of 80 milliseconds, while multi-criteria analysis methods, including the analytic hierarchy process, reached 88% accuracy. The integration of probabilistic models with adaptive algorithms enabled consideration of operational environment variability and timely risk assessment. In 2024, 45% of aviation incidents were associated with crew error, 24% with technical malfunctions, and 13% with weather conditions. Intelligent decision support systems could potentially prevent 75% of incidents related to procedural violations. Neural networks were most suitable for behavioral analysis, decision trees for access control, and Bayesian networks for assessing technical failures. The findings contribute to the development of intelligent data analysis methods and decision support algorithms for aviation safety.
References
[EASA] European Union Aviation Safety Agency (2025) European aviation environmental report 2025: executive summary and recommendations. [accessed July 1 2026]. https://www.easa.europa.eu/sites/default/files/eaer-downloads/EASA_EAER_2025_BROCHURE_WEB_%CE%95%CE%9D_v2.pdf
[FAA] Federal Aviation Administration (2020) FAA report on Aviation Safety Information Analysis and Sharing (ASIAS). [accessed July 1 2026]. https://www.faa.gov/sites/faa.gov/files/2021-11/FAA_Report_on_Aviation_Safety_Information_Analysis_and_Sharing_ASIAS_03312020.pdf
[ICAO] International Civil Aviation Organisation (2024) Safety report 2024. [accessed July 1 2026]. https://www.icao.int/sites/default/files/sp-files/safety/Documents/ICAO_SR_2024.pdf
Agrawal AV, Soni M, Keshta I, Savithri V, Abdinabievna PS, Singh S (2023) A probability-based fuzzy algorithm for multi-attribute decision-analysis with application to aviation disaster decision-making. Decis Anal J 8:100310. https://doi.org/10.1016/j.dajour.2023.100310
Ahmad A, Amjad R, Basharat A, Farhan AA, Abbas AE (2024) Fuzzy knowledge based intelligent decision support system for ground based air defence. J Ambient Intell Humaniz Comput 15(4):2317-2340. https://doi.org/10.1007/s12652-024-04757-3
Alharasees O, Kale U (2024) Aviation operators’ total loads analysis by multi-criteria decision-making. J Air Transp Manag 118:102596. https://doi.org/10.1016/j.jairtraman.2024.102596
Babak V, Kharchenko V, Vasylyev V (2007) Using generalized stochastic method to evaluate probability of conflict in controlled air traffic. Aviat 11(2):31-36. https://doi.org/10.3846/16487788.2007.9635958
Beisenbi M, Kaliyeva S (2018) Synthesis of the control systems by the state of an object with single input and single output by a gradient-velocity method of A.M. Lyapunov vector functions. Int J Civ Eng Technol 9 (10):2080-2086. https://iaeme.com/MasterAdmin/Journal_uploads/IJCIET/VOLUME_9_ISSUE_10/IJCIET_09_10_205.pdf
Bruno MF, Molfetta MG, Pratola L, Mossa M, Nutricato R, Morea A, Nitti DO, Chiaradia MT (2019) A combined approach of field data and earth observation for coastal risk assessment. Sensors (Switzerland) 19(6):1399. https://doi.org/10.3390/s19061399
Cankaya B, Topuz K, Delen D, Glassman A (2023) Evidence-based managerial decision-making with machine learning: the case of Bayesian inference in aviation incidents. Omega 120:102906. https://doi.org/10.1016/j.omega.2023.102906
Cui X, Zhang X, Zhao Z (2022) Real-time safety decision-making method for multirotor flight strategies based on TOPSIS model. Appl Sci 12(13):6696. https://doi.org/10.3390/app12136696
Degas A, Islam MR, Hurter C, Barua S, Rahman H, Poudel M, Ruscio D, Ahmed MU, Begum S, Rahman MA, et al. (2022) A survey on artificial intelligence (AI) and explainable AI in air traffic management: current trends and development with future research trajectory. Appl Sci 12(3):1295. https://doi.org/10.3390/app12031295
Deng Q, Santos BF, Verhagen WJ (2021) A novel decision support system for optimizing aircraft maintenance check schedule and task allocation. Decis Support Syst 146:113545. https://doi.org/10.1016/j.dss.2021.113545
Eleimat M, Őszi A (2025) Cybersecurity in aviation: exploring the significance, applications, and challenges of cybersecurity in the aviation sector. Period Polytech Transp Eng 53(2):169-183. https://doi.org/10.3311/PPtr.37153
Garcia AB, Babiceanu RF, Seker R (2021) Artificial intelligence and machine learning approaches for aviation cybersecurity: an overview. Paper presented 2021 Integrated Communications Navigation and Surveillance Conference. IEEE; Dulles, USA. https://doi.org/10.1109/ICNS52807.2021.9441594
Insaurralde CC, Blasch E (2022) Situation awareness decision support system for air traffic management using ontological reasoning. J Aerosp Inf Syst 19(3). https://doi.org/10.2514/1.I010989
Insaurralde CC, Blasch EP, Costa PC, Sampigethaya K (2022) Uncertainty-driven ontology for decision support system in air transport. Electronics 11(3):362. https://doi.org/10.3390/electronics11030362
Kale U, Alharasees O, Rohacs J, Rohacs D (2023) Aviation operators (pilots, ATCOs) decision-making process. Aircr Eng Aerosp Technol 95(3):442-451. https://doi.org/10.1108/AEAT-02-2022-0053
Kerimkhulle S, Azieva G, Saliyeva A, Mukhanova A (2022) Estimation of the volume of production of turbine vapor of a fuel boiler with stochastic exogenous factors. E3S Web Conf 339:02006. https://doi.org/10.1051/e3sconf/202233902006
Ketners K (2025) Adaptation of state security to modern military operations and terrorist risks in the world. Space Cult India 13(1):1-5. https://doi.org/10.20896/e3p2jm77
Kondratenko Y, Gerasin O, Topalov A (2016a) A simulation model for robot’s slip displacement sensors. Int J Comput 15 (4):224-236. https://doi.org/10.47839/ijc.15.4.854
Kondratenko YP, Kozlov OV, Gerasin OS, Zaporozhets YM (2016b) Synthesis and research of neuro-fuzzy observer of clamping force for mobile robot automatic control system. Paper presented 2016 IEEE First International Conference on Data Stream Mining & Processing. IEEE; Lviv, Ukraine. https://doi.org/10.1109/DSMP.2016.7583514
Lazic DA, Grujic V (2022) Resolving crisis situations in aviation from the past as a prediction for their successful overcoming today. South Florida J Dev 3(3):4014-4028. https://doi.org/10.46932/sfjdv3n3-073
Lee CY, Puranik TG, Mavris DN (2022) Deep spatio-temporal neural networks for risk prediction and decision support in aviation operations. J Comput Inf Sci Eng 21(4):041013. https://doi.org/10.1115/1.4049992
Li J, Dai J, Issakhov A, Almojil SF, Souri A (2021) Towards decision support systems for energy management in the smart industry and Internet of Things. Comput Ind Eng 161:107671. https://doi.org/10.1016/j.cie.2021.107671
Martinez-Marquez D, Pingali S, Panuwatwanich K, Stewart RA, Mohamed S (2021) Application of eye tracking technology in aviation, maritime, and construction industries: a systematic review. Sensors 21 (13):4289. https://doi.org/10.3390/s21134289
Maulana F, Starr A, Ompusunggu AP (2023) Explainable data-driven method combined with Bayesian filtering for remaining useful lifetime prediction of aircraft engines using NASA CMAPSS datasets. Machines 11(2):163. https://doi.org/10.3390/machines11020163
Mendes N, Vieira JGV, Mano AP (2022) Risk management in aviation maintenance: a systematic literature review. Saf Sci 153:105810. https://doi.org/10.1016/j.ssci.2022.105810
Milbredt O, Popa A, Doenitz FC, Hellmann M (2022) Aviation security automation: the current level of security automation and its impact. J Airpt Manag 16 (2): 184-208. https://doi.org/10.69554/ZFKK4145
Mizrak F, Akkartal GR (2024) Prioritizing cybersecurity initiatives in aviation: a DEMATEL-QSFS methodology. Heliyon 10 (16):e35487. https://doi.org/10.1016/j.heliyon.2024.e35487
Nogueira RP, Melicio R, Valério D, Santos LFFM (2023) Learning methods and predictive modeling to identify failure by human factors in the aviation industry. Appl Sci 13 (6): 4069. https://doi.org/10.3390/app13064069
Orazbayev B, Tanirbergenova A, Orazbayeva K, Berikbaeva M, Kaliyeva S, Kurmangaziyeva L, Makhatova V (2024) Decision making for control of the gasoline fraction hydrotreating process in a fuzzy environment. Proces 12 (4): 669. https://doi.org/10.3390/pr12040669
Ramos MA, Sankaran K, Guarro S, Mosleh A, Ramezani R, Arjounilla A (2023) The need for and conceptual design of an AI model-based Integrated Flight Advisory System. Proc Inst Mech Eng O J Risk Reliab 237 (2): 485-507. https://doi.org/10.1177/1748006X221083379
Rathnakumar R, Liu Y (2025) Towards safer general aviation operations using a vision-based decision support system for weather threat avoidance. J Air Transp Manag 123: 102709. https://doi.org/10.1016/j.jairtraman.2024.102709
Ruh ML, Sarojini D, Fletcher A, Asher I, Hwang JT (2023) Large-scale multidisciplinary design optimization of the NASA lift-plus-cruise concept using a novel aircraft design framework. arXiv. https://doi.org/10.48550/arXiv.2304.14889
Sekenov B, Smailov N, Tashtay Y, Amir A, Kuttybayeva A, Tolemanova A (2024) Fiber-optic temperature sensors for monitoring the influence of the space environment on nanosatellites: a review. Mech Mach Sci 167:371-380. https://doi.org/10.1007/978-3-031-67569-0_42
Singh S, Sharma SK, Parti R (2024) Role of aviation infrastructure in aviation operation. In: Khalid S, Siddiqui NN, editors. New innovations in AI, aviation, and air traffic technology. Hershey: IGI Global; p. 212-245. https://doi.org/10.4018/979-8-3693-1954-3.ch011
Smailov N, Nussupov Y, Taissariyeva K, Kuttybayev A, Baigulbayeva M, Turumbetov M, Hryhoriev Y, Lutsenko S (2025) Identification of dangerous situations in the road infrastructure using unmanned aerial vehicles. Technol Audit Prod Reserves 6 (2):97-102. https://doi.org/10.15587/2706-5448.2025.347074
Tang X, Lu X, Zheng P (2022) Aircraft autonomous separation assurance based on cooperative game theory. Aerospace 9 (8):421. https://doi.org/10.3390/aerospace9010042
Tashtay Y, Smailov N, Naubetov D, Sabibolda A, Nussupov Y, Kashkimbayeva N, Mailybayev Y, Batyrgaliyev A (2026) Fiberoptic gyroscopes: architectures, signal processing, error compensation, and emerging trends. J Sens Actuator Netw 15 (1):3. https://doi.org/10.3390/jsan15010003
Tashtay Y, Smailov N, Tazhen K, Sabibolda A, Nazarova A, Bazarbay A (2025) Paper presented 2025 International Conference on Electrical Engineering and Photonics. IEEE; Saint Petersburg, Russian Federation. https://doi.org/10.1109/EExPolytech66949.2025.11253802
The Boeing Company (2025) 2025 Chief Aerospace Safety Officer Report. [accessed July 1 2026]. https://www.boeing.com/content/dam/boeing/boeingdotcom/safety/caso/caso-report-2025.pdf
Ukwandu E, Ben-Farah MA, Hindy H, Bures M, Atkinson R, Tachtatzis C, Andonovic I, Bellekens X (2022) Cyber-security challenges in aviation industry: a review of current and future trends. Information 13 (3):146. https://doi.org/10.3390/info13030146
Vaneeva O, Kuriksha O, Sophocleous C (2015) Enhanced group classification of Gardner equations with time-dependent coefficients. Commun Nonlinear Sci Numer Simul 22 (1-3):1243-1251. https://doi.org/10.1016/j.cnsns.2014.09.016
Verma A, Singhal N (2023) Integrating artificial intelligence for adaptive decision-making in complex systems. In: Das S, Saha S, Coello CAC, Bansal JC, editors. Selected papers from ADCIS 2023: advances in data-driven computing and intelligent systems. Singapore: Springer. p. 95-105. https://doi.org/10.1007/978-981-99-9521-9_8
Vojtek N, Petrović B, Milošević P (2021) Decision support system for predicting the number of no-show passengers in airline industry. Tech J 28 (1):123-134. https://doi.org/10.17559/TV-20191215144655
Wang QA, Huang XY, Ni Y, Feng WS, Wang JF, Qiu S, Zhou P, Li JP, Luo Y (2026) Uncertainty-aware, high-precision multistep prediction of structural health monitoring sensor streams under extreme typhoon events: an enhanced Bayesian dynamic linear model leveraging the kernel regression basis function for severe environmental adaptation. Measurement 262.
https://doi.org/10.1016/j.measurement.2025.120050
Wójcik W, Kalizhanova A, Kulyk YA, Knysh BP, Kvyetnyy RN, Kulyk AI, Sichko TV, Dumenko VP, Bezstmertna OV, Adikhanova S, et al (2022) The method of time distribution for environment monitoring using unmanned aerial vehicles according to an inverse priority. J Ecol Eng 23 (11):179-187. https://doi.org/10.12911/22998993/153458
Xiong M, Wang H, Hou Z, Wong YD (2025) Multi-level information identification for civil aviation safety risks: a hierarchical multi-branch deep learning approach. Inf Sci 702:1218888. https://doi.org/10.1016/j.ins.2025.121888
Ziakkas D, Pechlivanis K (2023) Artificial intelligence applications in aviation accident classification: a preliminary exploratory study. Decis Anal J 9:100358. https://doi.org/10.1016/j.dajour.2023.100358
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