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System Architecture

Overview

The ClimateBackend system is designed as a modular, microservices-oriented architecture that integrates multiple data sources for climate monitoring. The system is built using Django and follows REST principles for API design.

Core Components

1. AWS Stations Module (awsstations)

  • Manages automated weather station data
  • Handles rainfall predictions
  • Processes station alerts and warnings
  • Integrates with machine learning models for predictions

2. Weather Stations Module (weatherstations)

  • Manages traditional weather station data
  • Tracks water levels and station status
  • Handles station image processing and storage
  • Provides historical water level analysis

3. Crowdsource Module (crowdsource)

  • Manages user-submitted flood reports
  • Handles location-based data collection
  • Provides data validation and verification
  • Integrates with mapping services

4. Database Middleware (dbmiddlelayer)

  • Provides centralized data access layer
  • Handles cross-source data integration
  • Implements data validation and processing
  • Manages system health monitoring

5. Dashboard Module (dashboard)

  • Unified data visualization interface
  • Server-side data filtering and processing
  • Multi-source data integration
  • Export capabilities for all data sources

Data Flow

                                   ┌─────────────────┐
                                   │                 │
                              ┌────►  AWS Stations   │
                              │    │                 │
                              │    └────────┬────────┘
                              │             │
┌─────────────────┐     ┌─────┴─────┐      │
│                 │     │           │      │
│    External     ├─────►    DB     ◄──────┤
│    Requests     │     │  Middleware│      │
│                 │     │           │      │
└─────────────────┘     └─────┬─────┘      │
                              │             │
                              │    ┌────────┴────────┐
                              │    │                 │
                              └────►Weather Stations │
                              │    │                 │
                              │    └────────┬────────┘
                              │             │
                              │    ┌────────┴────────┐
                              │    │                 │
                              └────► Crowdsource     │
                              │    │                 │
                              │    └────────┬────────┘
                              │             │
                              │    ┌────────┴────────┐
                              │    │                 │
                              └────► Dashboard       │
                                   │                 │
                                   └─────────────────┘

API Structure

Each module exposes its own REST API endpoints:

  • /dashboard/ - Main dashboard interface
  • /aws/* - AWS stations endpoints
  • /weather/* - Weather station endpoints
  • /cs/* - Crowdsource endpoints
  • /db/* - Database middleware endpoints

Database Schema

The system uses PostgreSQL with the following main tables:

AWS Stations

  • aws_station - Station information
  • rainfall_data - Rainfall measurements
  • predictions - Rainfall predictions

Weather Stations

  • weather_station - Station information
  • water_level - Water level measurements
  • station_images - Station monitoring images

Crowdsource

  • flood_report - User-submitted reports
  • location_data - Geographical information

Security

  • JWT-based authentication
  • Role-based access control
  • Input validation and sanitization
  • Rate limiting on API endpoints
  • CORS configuration for web clients

Performance Optimizations

  • Server-side data filtering and processing
  • Fixed data container sizes
  • Database query optimization
  • Caching with Redis
  • Asynchronous task processing with Celery
  • Efficient file storage and retrieval
  • API response pagination

Monitoring and Logging

  • System health monitoring
  • Error tracking and logging
  • Performance metrics collection
  • API usage statistics
  • Database query monitoring

Deployment

The system is designed to be deployed using:

  • Docker containers
  • Kubernetes orchestration
  • Load balancing
  • Auto-scaling capabilities
  • Continuous integration/deployment

Future Considerations

  • Enhanced machine learning integration
  • Real-time data processing
  • Advanced analytics dashboard
  • Mobile app integration
  • Extended API capabilities