✈️ Flight Accident Prediction Using Machine Learning
ABSTRACT
Aviation safety has always been a top priority in the airline industry, but traditional methods often fall short in identifying complex, real-time risk factors that can lead to flight accidents. To address this challenge, Flight Accident Prediction Using Machine Learning introduces AeroSafe, an intelligent, real-time prediction system powered by deep learning.
This system uses Convolutional Neural Networks (CNN) to analyze a comprehensive set of flight parameters — including aircraft type, crew experience, fuel level, runway conditions, and even live weather data. By training the model on a large synthetic dataset of flight records, AeroSafe can assess accident risk with high accuracy. It features both manual and real-time prediction modes, making it a powerful decision-support tool for pilots, airlines, and aviation safety authorities.
AeroSafe’s integration with live weather APIs allows for up-to-the-minute assessments. With a user-friendly web interface and automatic PDF report generation, the system provides not just accurate predictions, but also clear and actionable insights. This project sets a new benchmark in proactive flight safety management using AI.
EXISTING SYSTEM
Most current flight safety protocols rely heavily on:
- Static checklists
- Manual weather briefings
- Pilot experience and intuition
- Fragmented data sources (crew logs, aircraft records, weather data)
These systems have several key limitations:
- Cannot incorporate real-time changes
- Fail to detect complex, non-linear risk patterns
- Prone to human error and subjectivity
- Risk assessments are not quantifiable or standardized
- Manual processes delay critical decision-making
Overview of Flight Accident Prediction Techniques
With increasing complexity in aviation systems and weather unpredictability, machine learning offers a much-needed upgrade in how flight risks are analyzed.
Traditional Methods:
- ✅ Checklist-based assessment
- ✅ Historical weather briefings
- ❌ Not dynamic or predictive
- ❌ Prone to human bias
AI-Enhanced Approach:
- 📊 Data-driven and objective
- 🌐 Uses real-time weather data
- 🔍 Detects hidden, non-obvious risk patterns
- ⚙️ Learns and improves over time
Convolutional Neural Networks (CNNs) can detect subtle anomalies that rule-based systems might miss. Though traditionally used for images, CNNs here are adapted to structured flight data.
PROPOSED SYSTEM
AeroSafe is a complete, intelligent flight risk prediction platform with the following features:
- CNN-Based Prediction Engine: Learns from 2,500+ synthetic flight records
- Dual Prediction Modes: Manual Mode (25+ parameters), Real-Time Mode (live weather data)
- Flask-Based Web Interface: Modern design with Tailwind CSS
- Weather API Integration: Uses Open-Meteo API for real-time weather data
- PDF Report Generation: One-click downloadable risk assessments
MODULES
- User Authentication: Secure login, role-based access
- Preprocessing Module: Synthetic data, encoding, normalization
- Model Training: CNN model using TensorFlow/Keras
- Prediction Module: Accepts input and returns risk score
- Real-Time Prediction: Uses live weather data
- Visualization Module: Charts via Chart.js
- PDF Reporting Module: Generates flight safety reports
- Admin Dashboard: Logs, model updates, stats
ADVANTAGES
- 🌐 Real-Time Risk Prediction with live weather inputs
- 🧠 AI-Based Decision Support using CNNs
- 🧾 Instant Report Generation in PDF format
- 📊 Visual Risk Insights for better interpretation
- 🔐 Secure and Web-Based Flask app
- 🛡️ Proactive Planning and Awareness for pilots and crew
Software Requirements
- Frontend: HTML, CSS (Tailwind), JS
- Backend: Python 3.8, Flask
- ML Framework: TensorFlow/Keras
- Database: SQLite
- Weather API: Open-Meteo
Hardware Requirements
- Processor: i3 or above
- RAM: Minimum 4GB
- Hard Disk: 500GB
What You Get
- ✅ Base Paper
- ✅ Complete Source Code
- ✅ Project Documentation
- ✅ PPT Presentation
- ✅ Dataset & Preprocessing Scripts
- ✅ Screenshots and Execution Procedure
- ✅ UML Diagrams (Use Case, Sequence, Activity, ER)
- ✅ ReadMe & Installation Guide
- ✅ Add-ons and Supporting Tools
- ✅ Step-by-step Video Tutorial
📽️ Project Demo
CONCLUSION
The AeroSafe project brings predictive intelligence to aviation safety. It replaces outdated, manual risk assessment methods with a real-time, data-driven platform powered by CNNs. With its dual prediction modes and visual reports, AeroSafe is both practical and reliable.
By identifying flight risks before takeoff, the system empowers airline staff to make safer and smarter decisions. AeroSafe represents a proactive shift in aviation safety — driven by artificial intelligence.
📩 Get This Project
- 🌐 Website: https://www.ieeexpert.com
- 📧 Email: xpertieee@gmail.com
- 📺 YouTube: youtube.com/@ieeexpert4921
