Designing an intelligent model to optimize the safety risk of the takeoff flight using BIM-LSTM

Subject Areas : Journal of Investment Knowledge

mansour yahyavi ¹ , Abbass toloie eshlaghi ² , Mohammad Ali Afsharkazemi ³ , Reza Radfar ⁴

1 - Ph.D .Student, Department of Information Technology Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Professor, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - professor ,Department Information Technology Management, Central Tehran Branch, Islamic Azad University, Tehran
4 - professor, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran,

Received: 2023-08-14 Accepted : 2023-08-23 Published : 2024-12-21

Keywords: "Optimization", "BIM-LSTM model", "Flight Safety Risk",

Abstract :

This article presents a new model for optimizing the safety risk of take-off, as the most important and dangerous flight process, using a combination of BI algorithm and recurrent neural network LSTM. The goal is to train an effective neural network with past data records of air accidents to predict safety risk parameters. For this purpose, 17 safety features, such as weather conditions, aircraft configuration and preparation, flight information and air traffic were obtained. The data related to 2019 to 2020 was selected after performing exploration, summarization, cleaning, normalization operations with 28813 data records. Due to the dependence of flight data on their previous inputs and the need for a kind of memory, training was performed by deep learning algorithm (LSTM) in Python environment. After learning, the learning error was about 6 percent and the mean square error was about 116/0. It shows that the error percentage is negligible and the proposed model has high validity. Also, this model solved the problem of exploration and cleaning of bulk flight data by having advanced tools such as ETL, metadata and real-time monitoring and was able to predict the most important safety risk factor (speed V1) with high accuracy. This pattern helps the flight service in controlling the important parameters of safety risk, such as the speed of aircraft taking off from the runway, controlling the safe take-off speed and most importantly controlling the loss of flight with a reliable strategy.

References:

• Aleksandrs Bitiņš, Ruta Bogdane, Vladimir Shestakov, Anastasija Stepanova (2022)the ORETICAL AND METHODOLOGICAL APPROACHES TO THE INFORMATION BASE FOR AN AIRLINE’S FLIGHT SAFETY SYSTEM Transactions on Aerospace Research eISSN 2545-2835, VOL. 266, NO. 1/2022, 75-83 DOI: 10.2478/tar-2022-0006


• Balachandran. S and Ella. M. Atkins (2015) Flight Safety Assessment and anagement for Takeoff Using Deterministic Moore Machines JOURNAL OFAEROSPACE INFORMATION SYSTEMS Vol. 12, No. 9, September


• Baars, H. & Kemper, H.-G. (2008).“Management Support with Structured and Unstructured Data: An Integrated Business Intelligence Framework,” Information Systems Management, 25(2). 132-148.


• Borst, C., Grootendorst, F. H., Brouwer, D. I. K., Bedoya, C., Mulder, M., and van Paassen, M. M., “Design and Evaluation of a Safety Augmentation System for Aircraft,” Journal of Aircraft, Vol. 51, No. 1, 2013, pp. 12–22. doi:10.2514/1.C031500


• P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.


• EDAN HABLER, RON BITTON, and ASAF SHABTAI(2021) Evaluating the Security of Aircraft Systems arXiv:2209.04028v1 [cs.CR] 8 Sep 2022


• Eduardo Gallo(2021) Quasi Static Atmospheric Model for Aircraft Trajectory Prediction and Flight Simulation Systems and Control (eess.SY) arXiv:2101.10744v1 [eess.SY] 26 Jan 2021


• A. Gers, J. Schmidhuber, and F. Cummins,(1999) “Learning to forget: Continual prediction with lstm,” 1999.


• Gabriel Jarry, Daniel Delahaye, Eric Féron.(2020) Approach and landing aircraft on-board parameters estimation with LSTM networks. AIDA-AT 2020, 1st conference on Artificial Intelligence and Data Analytics in Air Transportation, Feb 2020, Singapore, Singapore. pp.ISBN: 978-1-7281-5381-0


• Govindarajan, N., De Visser, C., Van Kampen, E., Krishnakumar, K., Barlow, J., and Stepanyan, V., “Optimal Control Framework for Estimating Autopilot Safety Margins,” Journal of Guidance, Control, and Dynamics, Vol. 38, No. 7, 2015, pp. 1197–1207. doi:10.2514/1.G000271


• Guo Y, Sun Y (2020) Flight safety assessment based on an integrated human reliability quantification approach. PLoS ONE 15(4): e0231391. https://doi.org/10.1371/journal. pone.0231391


• He, Z.; Zhou, J.; Dai, H.N.; Wang, H. Gold Price Forecast Based on LSTM-CNN Model. In Proceedings of the 2019 IEEE Intl Conference on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/ CyberSciTech), Fukuoka, Japan, 5–8 August 2019; pp. 1046–1053.


• ICAO, Doc 9859, Safety management manual, 4th ed. Montréal, Quebec, Canada: International Civil Aviation Organization (ICAO), 2022.


• Jing Lu, Longfei Pan, Jingli Deng, Hongjun Chai1, Zhou Ren1 and Yu Shi(2022)Deep learning for Flight Maneuver Recognition: A survey ERA, 31(1): 75–102. DOI: 10.3934/era.2023005


• Johar Samosir, Sarinah Sihombing,  Hendro Kuntohadi ()  Effect of Effectiveness of Use of Electronic Flight Bags on Flight Safety at PT. Garuda Indonesia Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021, Pages. 112 - 122 Received 16 Fe bruary 2021; Accepted 08 March 2021.


• JuanFang, QiangangZheng, ChangpengCai, HaoyinChen, HaiboZhang (2023) Deep reinforcement learning method for turbofan engine acceleration optimization problem within full flight envelope  Aerospace Science and Technology [m5G; v1.333] P.1 (1-14)


• Mickael Rey, Daniel Aloise , François Soumis ,Romanic Pieugueu (2021)


• Ng Iris, Sarasvathi Nagalingham (2023) Implementation of Business Intelligence Solution for United Airlines (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 14, No. 1, 2023


• PeiyaoWang, Mingxin Yu1 , Guang Yan1, Jiabin Xia, Jiawei Liu1and Lianqing Zhu(2023) A deep learning-based method for calculating aircraft wing loads Measurement and Control 1–13 _ The Author(s) 2023  Article reuse guidelines: sagepub.com/journals-permissionsDOI: 177/00202940221145971journals.sagepub.com/home/mac


• Singh, G.; Singh, J.; Prabha, C. Data visualization and its key fundamentals: A comprehensive survey. In Proceedings of the 20227th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 22–24 June 2022.


• Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” The Mathematical Intelligencer, vol. 27, no. 2, pp. 83–85, 2005


• Tahsin Sejat Saniat, Tahiat Goni, Shaikat M. Galib (2020) LSTM RECURRENT NEURAL NETWORK ASSISTED AIRCRAFT STALL PREDICTION FOR ENHANCED SITUATIONAL AWARENESS arXiv:2012.04876v1 [cs.LG] 9 Dec 2020


• Yi Lin , Linjie Deng, Zhengmao Chen, Xiping Wu, Jianwei Zhang, and Bo Yang(2020) A Real-Time ATC Safety Monitoring nramework Using a Deep Learning Approach IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 21, NO. 11, NOVEMBER 2020


• Zammit-Mangion, D., and Eshelby, M., “Simplified Algorithm to Model Aircraft Acceleration During Takeoff,” Journal of Aircraft, Vol. 45, No. 4, 2008, pp. 1090–1097. doi:10.2514/1.22966


• Zhu, D.; Wang, Y.; Zhang, F. Energy Price Prediction Integrated with Singular Spectrum Analysis and Long Short-Term Memory Network against the Background of Carbon Neutrality. Energies 2022, 15, 8128. [CrossRef]