Disaster Relief Management System

By, Team ResQAI (PEC Hacks - Hackathon project)

Overview

The Disaster Relief Management System is an integrated platform designed to optimize emergency response operations during natural disasters. This system combines real-time evacuation route optimization, overhead people detection using drone technology, and a centralized complaint management system to enhance disaster response coordination and save lives.

🚀 Key Features

🗺️ Evacuation Route Optimization

• Real-time traffic analysis and route optimization
• Machine learning-powered road closure prediction using social media data
• Interactive mapping with Google Maps API integration
• Live wildfire tracking and evacuation center locations

🚁 Overhead People Detection

• AI-powered people detection from drone footage
• Real-time location tracking of individuals in distress
• Automated counting and coordinate reporting
• Streamlit-based web interface for monitoring

📋 Complaint Management System

• Centralized disaster-related complaint handling
• Auth0 integration for secure authentication
• Admin and user role management
• Firebase backend for data persistence

🏗️ System Architecture

Disaster-Relief/
├── EvacuationRoutes/          # Route optimization and traffic analysis
├── overhead-people-detection/  # Drone-based people detection
├── Complaints/                # Web-based complaint management
└── README.md                  # Project documentation

🔧 Installation & Setup

Prerequisites

• Python 3.8+
• Node.js (for frontend dependencies)
• Firebase account
• Google Maps API key
• Auth0 account

🚁 Evacuation Routes Module

Features

• Real-time Traffic Analysis: Integration with Google Maps API for live traffic data
• ML-powered Predictions: Logistic regression model (93% accuracy) for road closure prediction
• Social Media Integration: Twitter data analysis using Tweepy and GetOldTweets3
• Interactive Mapping: Visual representation of fire locations, evacuation centers, and optimal routes

Key Technologies

• Google Maps API
• Tweepy & GetOldTweets3
• Scikit-learn
• Pandas & NumPy
• Flask

👥 People Detection Module

Features

• Computer Vision: Real-time people detection from overhead drone footage
• Location Tracking: Precise coordinate reporting for rescue operations
• Live Monitoring: Webcam integration with 2-second detection intervals
• Web Interface: Streamlit-based dashboard for monitoring operations

Key Technologies

• OpenCV
• TensorFlow/PyTorch
• Streamlit
• NumPy

Performance

• Real-time processing capability
• Automatic video source detection
• Configurable detection intervals

📊 Complaints Management Module

Features

• Secure Authentication: Auth0 integration for user management
• Role-based Access: Separate admin and user interfaces
• Real-time Data: Firebase backend for instant updates
• Responsive Design: Mobile-friendly web interface

Key Technologies

• Django 4.0+
• Auth0
• Firebase/Firestore
• HTML/CSS/JavaScript

User Roles

• Admin: Full access to complaint management and system administration
• User: Submit and track disaster-related complaints

📈 Performance Metrics

• Route Optimization: Real-time processing with sub-second response times
• ML Accuracy: 93% accuracy in road closure prediction (vs 77% baseline)
• Detection Speed: Real-time people detection at 2-second intervals
• System Availability: 99.9% uptime target for emergency operations

🔮 Future Enhancements

Planned Features

• Maximum Flow Algorithm: Implementation of advanced routing algorithms for dynamic path optimization
• Enhanced ML Models: Deep learning models for improved prediction accuracy
• Drone Integration: Autonomous drone control and food/medicine delivery capabilities
• Mobile Applications: Native iOS and Android apps for field personnel
• Multi-language Support: International disaster response capabilities

Technical Improvements

• API Rate Limiting: Enhanced API access for emergency situations
• Real-time Validation: Ground-truth verification system for predictions
• Scalability: Microservices architecture for handling large-scale disasters

🤝 Contributing

We welcome contributions to improve the Disaster Relief Management System. Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Original Evacuation Routes Development: Wilson Stewart, Alaa Senjab & Djuwita Carney
• Data Sources: NASA FIRMS, Cal Fire, Sonoma County Emergency Services
• APIs: Google Maps, Twitter, Firebase, Auth0

📞 Support

For technical support or questions about the system:

• Create an issue in this repository
• Contact the development team

---

Built with ❤️ for disaster relief and emergency response operations