طراحی مدلی هوشمند جهت بهینه‌سازی ریسک ایمنی پرواز تیک‌آف با استفاده از ‌BIM-LSTM

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری تخصصی، گروه مدیریت فناوری اطلاعات، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

2 استاد گروه مدیریت فناوری اطلاعات، دانشکده مدیریت، واحد علوم تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

3 استاد گروه مدیریت فناوری اطلاعات، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

10.30495/jik.2024.23254

چکیده

مقاله حاضر، مدل جدیدی جهت بهینه‌سازی ریسک ایمنی تیک‌آف به عنوان مهم‌ترین و خطرناکترین فرایند پرواز، با استفاده از ترکیب الگوریتم BI‌ و شبکه عصبی بازگشتی LSTM ارایه می‌دهد. هدف، آموزش یک شبکه عصبی موثر با رکوردهای داده‌ سوانح هوایی گذشته برای پیش‌بینی پارامترهای ریسک ایمنی است. بدین منظور ۱۷ ویژگی ایمنی، مانند شرایط آب و هوایی، پیکربندی و آماده‌سازی هواپیما، اطلاعات پرواز و ترافیک هوایی بدست آمد. داده مربوطه از سال ۲۰۱۹ تا ۲۰۲۰ پس از انجام عملیات اکتشاف، خلاصه‌سازی، پاکسازی، نرمال‌سازی به تعداد ۲۸۸۱۳ رکورد داده انتخاب شد. به علت وابستگی داده‌های پرواز به ورودی‌های ما قبل خود و نیاز به نوعی حافظه، آموزش توسط الگوریتم یادگیری عمیق (LSTM) در محیط پایتون انجام گرفت. پس از یادگیری، خطای یادگیری حدود ۶ درصد و میانگین مربعات خطا حدود۱۱۶/۰ بدست آمد. نشان می‌دهد، درصد خطا غیر قابل توجه و مدل پیشنهادی از اعتبار بالایی برخوردار است. هم‌چنین این مدل به دلیل برخورداری از ابزارهای پیشرفته از جمله ETL، متادیتا و مانیتوریگ لحظه‌ای مشکل اکتشاف و پاکسازی انبوه داده‌های پرواز را حل کرد و توانست مهم‌ترین عامل ریسک ایمنی (سرعت V1 ) را با دقت بالا پیش‌بینی‌ کند. این الگو با راهبردی قابل اعتماد به خدمه پرواز در راستای کنترل پارامترهای مهم ریسک ایمنی از جمله، سرعت بلند شدن هواپیما از باند، کنترل سرعت ایمن تیک‌آف و مهم‌تر از همه کنترل از دست رفتن پرواز کمک می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

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

نویسندگان [English]

  • mansour yahyavi 1
  • Abbass toloie eshlaghi 2
  • Mohammad Ali Afsharkazemi 3
  • Reza Radfar 2
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
چکیده [English]

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.

کلیدواژه‌ها [English]

  • "Flight Safety Risk"
  • "BIM-LSTM model"
  • "Optimization"

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