A comprehensive, production-ready air quality prediction system that uses satellite imagery, IoT sensor data, and weather information to predict Air Quality Index (AQI) using advanced machine learning and deep learning models.

• Real-time Air Quality Data: OpenAQ API integration with global coverage
• Satellite Imagery Processing: NASA Earthdata Sentinel-2 imagery analysis
• Weather Data Fusion: OpenWeatherMap historical and forecast data
• IoT Sensor Integration: Support for custom sensor data sources
• Traffic & Urban Data: OpenStreetMap and traffic pattern analysis

• CNN-LSTM Hybrid: Deep learning model combining convolutional and recurrent networks
• Ensemble Methods: Random Forest, Gradient Boosting with hyperparameter optimization
• Time Series Models: LSTM, GRU for temporal pattern recognition
• Transfer Learning: Pre-trained models for satellite image analysis
• AutoML Integration: Automated model selection and hyperparameter tuning

• Interactive Dashboards: Plotly-based real-time monitoring dashboards
• Geographic Mapping: Folium-powered interactive maps with AQI overlays
• Time Series Analysis: Advanced trend analysis and seasonality detection
• Correlation Analysis: Multi-dimensional pollutant and weather correlations
• Predictive Analytics: Forecast models with confidence intervals

• Live Data Streaming: Real-time AQI monitoring and alerts
• Instant Predictions: Sub-second AQI predictions for any location
• API Endpoints: RESTful API for integration with other systems
• WebSocket Support: Real-time data streaming for web applications
• Mobile App Ready: JSON API responses for mobile development

• Modular Architecture: Plug-and-play component system
• Configurable Models: Easy model parameter tuning via config files
• Multi-City Support: Simultaneous monitoring of multiple cities
• Custom Feature Engineering: Extensible feature creation pipeline
• Plugin System: Support for custom data sources and models

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Data Sources  │    │  Preprocessing  │    │  ML/DL Models   │
│                 │    │                 │    │                 │
│ • OpenAQ API    │──▶│ • Data Cleaning │───▶│ • CNN-LSTM      │
│ • Weather API   │    │ • Feature Eng.  │    │ • Random Forest │
│ • Satellite     │    │ • Normalization │    │ • Gradient Boost│
│ • IoT Sensors   │    │ • Sequence Gen. │    │ • SVR           │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │                       │
                                ▼                       ▼
                       ┌─────────────────┐    ┌─────────────────┐
                       │   Validation    │    │  Visualization  │
                       │                 │    │                 │
                       │ • Cross-valid.  │    │ • Interactive   │
                       │ • Hyperparam.   │    │ • Maps & Charts │
                       │ • Model Select. │    │ • Dashboards    │
                       └─────────────────┘    └─────────────────┘

• Coverage: Global, 10,000+ monitoring stations
• Pollutants: PM2.5, PM10, NO2, SO2, CO, O3, VOCs
• Update Frequency: Real-time to hourly
• Historical Data: Up to 5 years of historical measurements
• No API Key Required: Completely free access

• Parameters: Temperature, humidity, pressure, wind, precipitation
• Forecast: 5-day weather predictions
• Historical: Up to 5 years of historical weather data
• Free Tier: 1,000 API calls per day
• Global Coverage: 200,000+ cities worldwide

• Platforms: Sentinel-2, Landsat, MODIS
• Bands: RGB, NIR, SWIR for comprehensive analysis
• Resolution: 10m to 1km spatial resolution
• Temporal: Daily to monthly updates
• Completely Free: No usage limits

• Traffic Data: OpenStreetMap, Google Maps Traffic
• Urban Planning: Building density, green space coverage
• Demographic Data: Population density, industrial areas
• Economic Indicators: Industrial activity, transportation

1. CNN-LSTM Hybrid Architecture

   • CNN Layers: Feature extraction from satellite imagery
   • LSTM Layers: Temporal dependency modeling
   • Attention Mechanisms: Focus on relevant time periods
   • Multi-Head Architecture: Parallel processing of different data types

2. Advanced LSTM Variants

   • Bidirectional LSTM: Forward and backward temporal analysis
   • Stacked LSTM: Multiple LSTM layers for complex patterns
   • Attention LSTM: Focus on important time steps
   • GRU Networks: Gated Recurrent Units for efficiency

3. Satellite Image Processing

   • Convolutional Neural Networks: Image feature extraction
   • Transfer Learning: Pre-trained ResNet, VGG models
   • Multi-Spectral Analysis: RGB + NIR band processing
   • Object Detection: Urban area and pollution source identification

1. Ensemble Methods

   • Random Forest: 100+ trees with feature importance
   • Gradient Boosting: Adaptive boosting with regularization
   • XGBoost: Extreme gradient boosting optimization
   • LightGBM: Light gradient boosting for large datasets

2. Advanced Regression

   • Support Vector Regression: Kernel-based non-linear regression
   • Elastic Net: L1 + L2 regularization
   • Polynomial Regression: Non-linear feature relationships
   • Ridge/Lasso Regression: Regularized linear models

3. Time Series Models

   • ARIMA: Auto-regressive integrated moving average
   • Prophet: Facebook's forecasting tool
   • Exponential Smoothing: Holt-Winters method
   • VAR: Vector auto-regression for multiple variables

• Temporal Features: Hour, day, month, season, holidays
• Cyclical Encoding: Sin/cos transformations for time
• Lag Features: Previous hour/day AQI values
• Rolling Statistics: Moving averages, standard deviations
• Cross-Features: Interaction between weather and pollutants

• Outlier Detection: IQR, Z-score, Isolation Forest
• Missing Data Imputation: Multiple imputation strategies
• Data Validation: Schema validation and consistency checks
• Quality Metrics: Completeness, accuracy, timeliness scores

• Regression Metrics: RMSE, MAE, R², MAPE
• Time Series Metrics: MASE, SMAPE, RMSSE
• Classification Metrics: Accuracy, Precision, Recall, F1
• Business Metrics: Prediction accuracy by AQI category

• Real-Time Monitoring: Live AQI updates with alerts
• Multi-City Comparison: Side-by-side city analysis
• Trend Analysis: Long-term AQI patterns and seasonality
• Forecast Visualization: Future AQI predictions with confidence

• Heat Maps: AQI concentration across geographic areas
• Choropleth Maps: Administrative boundary-based AQI display
• 3D Terrain Maps: Elevation-based AQI analysis
• Satellite Overlays: AQI data overlaid on satellite imagery

• Multi-Axis Plots: Multiple variables on different scales
• Box Plots: Distribution analysis by time periods
• Correlation Heatmaps: Multi-dimensional relationships
• Time Series Decomposition: Trend, seasonal, and residual components

# Model Configuration
MODEL_CONFIG = {
    'cnn_lstm': {
        'cnn_filters': [64, 128, 256],
        'lstm_units': [128, 64],
        'dropout_rate': 0.3,
        'learning_rate': 0.001
    },
    'random_forest': {
        'n_estimators': 200,
        'max_depth': 15,
        'min_samples_split': 5
    }
}

# Data Collection Settings
DATA_CONFIG = {
    'update_frequency': '1h',
    'retention_period': '2y',
    'quality_threshold': 0.8,
    'backup_enabled': True
}

• Custom Data Sources: Easy integration of new APIs
• Custom Models: Add your own ML/DL algorithms
• Custom Visualizations: Extend the visualization system
• Custom Metrics: Define business-specific evaluation criteria

• Parallel Processing: Multi-threaded data collection
• Memory Management: Efficient data structures and caching
• Model Compression: Quantization and pruning for deployment
• Batch Processing: Efficient handling of large datasets

• Microservices Architecture: Modular, scalable design
• Database Integration: Support for PostgreSQL, MongoDB
• Cloud Deployment: AWS, Azure, GCP ready
• Container Support: Docker and Kubernetes deployment

# AQI Prediction API
POST /api/v1/predict
{
    "city": "Delhi",
    "coordinates": {"lat": 28.6139, "lon": 77.2090},
    "model": "cnn_lstm",
    "forecast_hours": 24
}

# Data Collection API
GET /api/v1/data/{city}
GET /api/v1/data/{city}/pollutants
GET /api/v1/data/{city}/weather

• Real-time Updates: Live AQI monitoring
• Alert Notifications: Instant pollution alerts
• Data Streaming: Continuous data flow
• Multi-client Support: Multiple dashboard connections

• CSV/Excel: Tabular data export
• JSON: API responses and data exchange
• GeoJSON: Geographic data for mapping
• PDF Reports: Automated report generation

• API Key Management: Secure storage of API credentials
• Data Encryption: At-rest and in-transit encryption
• Access Control: Role-based permissions
• Audit Logging: Complete activity tracking

• Data Anonymization: Remove personal identifiers
• Consent Management: GDPR compliance features
• Data Retention: Configurable data lifecycle
• Right to Deletion: Complete data removal capability

• API Reference: Complete endpoint documentation
• User Guides: Step-by-step tutorials
• Developer Docs: Code examples and best practices
• Video Tutorials: Visual learning resources

• GitHub Issues: Bug reports and feature requests
• Discord Community: Real-time support and discussion
• Documentation Wiki: Community-contributed guides
• Code Examples: Sample implementations and use cases

• Air Quality Monitoring: Real-time city-wide monitoring
• Policy Making: Data-driven environmental policies
• Public Health: Early warning systems for vulnerable populations
• Urban Planning: Pollution-aware city development

• Epidemiological Studies: Air quality and health correlations
• Clinical Research: Patient exposure assessment
• Public Health: Community health impact analysis
• Preventive Medicine: Risk assessment and recommendations

• Supply Chain: Route optimization for clean air
• Real Estate: Property value and air quality correlation
• Insurance: Risk assessment for health policies
• Tourism: Destination air quality information

• Personal Health: Daily air quality monitoring
• Outdoor Activities: Exercise timing optimization
• Travel Planning: Destination air quality research
• Home Automation: Smart ventilation systems

• AI-Powered Forecasting: Advanced prediction algorithms
• Mobile Applications: iOS and Android apps
• IoT Device Integration: Smart sensor networks
• Blockchain Integration: Decentralized data verification

• Edge Computing: Local processing capabilities
• 5G Integration: High-speed data transmission
• Augmented Reality: AR visualization of air quality
• Voice Assistants: Alexa and Google Home integration

• Quantum Computing: Quantum ML algorithms
• Satellite Constellation: Custom satellite network
• Global Coverage: 100% worldwide monitoring
• Predictive Maintenance: Equipment failure prediction

# Clone repository
git clone https://github.com/yourusername/aqi-predictor.git
cd aqi-predictor

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/

# Code formatting
black .
isort .

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Make changes and add tests
4. Run test suite: pytest tests/
5. Submit pull request with detailed description

• Code Quality: Black, isort, flake8, mypy
• Testing: pytest, coverage, hypothesis
• CI/CD: GitHub Actions, automated testing
• Documentation: Sphinx, Read the Docs

• MIT License: Permissive open source license
• Commercial Use: Free for commercial applications
• Modification: Modify and distribute freely
• Attribution: Credit to original authors

• OpenAQ Data: CC0 public domain
• Weather Data: OpenWeatherMap terms of service
• Satellite Data: NASA open data policy
• User Data: GDPR compliant privacy protection

• Scikit-learn: Machine learning algorithms
• TensorFlow: Deep learning framework
• Pandas: Data manipulation and analysis
• Matplotlib/Seaborn: Data visualization
• Plotly: Interactive plotting library
• Folium: Python mapping library

• OpenAQ: Global air 7BCA quality data
• OpenWeatherMap: Weather data API
• NASA Earthdata: Satellite imagery
• OpenStreetMap: Geographic data

• Academic Papers: Methodology and algorithms
• Open Research: Collaborative development
• Peer Review: Quality assurance and validation
• Scientific Method: Evidence-based approach

The system uses environment variables for secure configuration:

1. Copy environment template:

   cp config/.env.example config/.env

2. Configure your API keys in config/.env:

   # OpenWeatherMap API
   OPENWEATHER_API_KEY=your_api_key_here
   
   # NASA Earthdata
   NASA_USERNAME=your_username
   NASA_PASSWORD=your_password

3. Never commit .env files to version control

• ✅ API Key Protection: All secrets stored in environment variables
• ✅ Git Ignore: Sensitive files automatically excluded
• ✅ No Hardcoded Secrets: Configuration loaded securely
• ✅ Security Validation: Run python scripts/security_check.py to verify

Ready to build your own air quality prediction system? Follow our quick start guide:

1. Install Dependencies: python scripts/install_packages.py
2. Configure Environment: Copy config/.env.example to config/.env and add your API keys
3. Run Demo: python examples/simple_demo.py
4. Full System: python main.py
5. Security Check: python scripts/security_check.py

Remember: This system uses only free APIs and datasets. No payment required!

For questions, support, or contributions:

• 📧 Email: support@aqipredictor.com
• 💬 Discord: Join our community
• 🐛 Issues: GitHub Issues
• 📖 Docs: Read the Docs

Star ⭐ this repository if you find it helpful!