Real-time Beach Safety Monitoring with AI-Powered Risk Assessment
A comprehensive beach safety monitoring system that combines real-time weather tracking, AI-driven risk analysis, and community reporting to prevent accidents and make beach safety data accessible and actionable for beachgoers and tourists.
The Challenge: Beachgoers lack real-time, actionable safety information, leading to preventable accidents and poor decision-making about water activities.
Our Solution: AquaSafe provides instant access to comprehensive beach conditions, AI-powered risk assessments, community-reported incidents, and emergency protocols—all in one integrated platform.
- 🌦️ Real-Time Weather Monitoring - Live weather data, water conditions, and environmental metrics
- 🤖 AI-Powered Risk Assessment - Machine learning analysis of incident patterns and risk factors
- 👥 Community Reporting - User-generated incident reports and safety observations
- 🚨 Emergency Protocols - Quick access to emergency procedures and contact information
- ⚡ Real-Time Alerts - WebSocket-based instant notifications for safety updates
- 📍 Geospatial Data - Location-based beach monitoring and incident mapping
- 🔐 Secure Authentication - JWT-based authentication with bcrypt encryption
- React 18 with TypeScript for type-safe, scalable UI
- Vite for lightning-fast development and builds
- React Query (@tanstack/react-query) for efficient state management
- CSS with responsive design patterns
- Node.js & Express.js with comprehensive RESTful API
- MongoDB with Mongoose ODM for flexible schema design
- Redis for high-performance caching and optimization
- Socket.io for real-time WebSocket communication
- JWT + bcrypt for secure authentication
- Python ML Service for advanced incident analysis (port 8000)
- Docker Compose for containerized development and deployment
- Google Cloud Storage for file management
- SendGrid for email notifications
- Bull job queue for asynchronous task processing
- Express Rate Limiting for API protection
- Joi for comprehensive request validation
- Circuit Breaker Patterns for fault tolerance
- Extensive Error Handling & Monitoring
- Node.js (v18 or higher)
- MongoDB (local or cloud instance)
- Redis (local or cloud instance)
- Python 3.8+ (for ML service)
- Google Cloud Storage credentials
- SendGrid API key
-
Clone the repository
git clone https://github.com/kasimlohar/AquaSafe.git cd AquaSafe -
Install dependencies
# Install backend dependencies cd backend && npm install # Install frontend dependencies cd ../frontend && npm install cd ..
-
Set up environment variables
# Backend environment variables (.env) MONGODB_URI=your_mongodb_connection_string REDIS_URL=your_redis_connection_string JWT_SECRET=your_jwt_secret GOOGLE_CLOUD_STORAGE_CREDENTIALS=your_gcs_credentials SENDGRID_API_KEY=your_sendgrid_key SESSION_SECRET=your_session_secret -
Start services
# MongoDB and Redis should be running # You can use Docker Compose or run them separately
-
Seed database
npm run seed
Development Mode:
# Run all services concurrently
npm run devIndividual Services:
# Backend (http://localhost:3001)
cd backend && npm start
# Frontend (http://localhost:3000)
cd frontend && npm run dev
# ML Service (http://localhost:8000)
cd ml-service && python app.pyVisit http://localhost:3000 to access the application.
AquaSafe/
├── frontend/ # React + Vite application
│ └── src/
│ ├── components/ # Reusable React components
│ └── main.jsx # Application entry point
├── backend/ # Node.js + Express API
│ └── src/
│ ├── models/ # MongoDB schemas
│ ├── routes/ # API endpoints
│ ├── middleware/ # Validation & authentication
│ └── server.js # Server entry point
├── ml-service/ # Python ML service
├── docs/ # Comprehensive documentation
│ ├── API.md # API design & endpoints
│ ├── DATABASE.md # Database schema
│ └── DEPLOYMENT.md # Deployment guides
└── scripts/ # Setup & utility scripts
The backend provides a comprehensive RESTful API with the following main endpoints:
- Beach Data - Retrieve real-time beach conditions and metrics
- Incident Management - Create, read, update incident reports
- User Management - User registration, authentication, profiles
- Real-Time Alerts - WebSocket-based safety notifications
- Risk Assessment - AI-powered risk analysis and recommendations
For detailed API documentation, visit /docs/API.md in the repository.
Access the test page at /test to view and interact with UI components during development.
Why MongoDB? Flexible schema design accommodates varied incident reports, geospatial data, and dynamic beach monitoring information without rigid table structures.
Why Redis Caching? Dramatically improves performance for frequently accessed beach data and reduces database load during peak usage.
Why WebSockets? Real-time incident alerts and safety updates require instant communication without polling overhead.
Why Microservices? Separation of ML analysis into a dedicated Python service enables independent scaling and easier maintenance.
Production-Ready Features:
- Comprehensive validation on all inputs
- Circuit breaker patterns for external service resilience
- Error handling and structured logging
- Extensive test coverage
This project deepened my expertise in several critical areas:
- Full-Stack Architecture - Building scalable, production-ready systems with proper separation of concerns
- Real-Time Systems - Implementing WebSocket communication and handling concurrent connections efficiently
- Performance Optimization - Strategic caching with Redis and query optimization techniques
- Comprehensive Validation - Multi-layer validation for data integrity and security
- ML Integration - Connecting machine learning services with production applications
- DevOps Practices - Docker containerization and deployment strategies
Proper caching strategies can reduce database load by 80%+. Comprehensive error handling transforms user experience. Modular architecture enables fearless scaling. Real-time systems require careful state management.
Current Progress:
- ✅ Core API and database schema complete
- ✅ Frontend component library built
- ✅ WebSocket real-time features operational
- ✅ Redis caching implemented
- 🔄 ML service integration in progress
- 🔄 Deployment pipeline being finalized
Planned Features:
- Complete ML model training and integration
- Native mobile application (React Native)
- Advanced analytics and insights dashboard
- Prediction models for peak safety hours
- Integration with official weather APIs
Known Limitations:
- ML service integration currently in development
- Live deployment to production pending
- Mobile app development to begin Q4
Comprehensive deployment guides are available in /docs/DEPLOYMENT.md, including:
- Docker containerization and orchestration
- Environment configuration for production
- Database migration strategies
- CI/CD pipeline setup
AquaSafe welcomes contributions! The codebase features:
- Well-documented API with clear endpoint specifications
- Comprehensive validation middleware
- Extensive test coverage for reliability
- Modular architecture for easy feature additions
This project is licensed under the MIT License - see the LICENSE file for details.
Kasim Lohar
- GitHub: @kasimlohar
- LinkedIn: Kasim Lohar
- Email: kasimlohar2@gmail.com
Built as a comprehensive demonstration of full-stack development, real-time systems, and AI integration. Special focus on production-ready code quality, security, and performance optimization.
Made with ❤️ for safer beaches