🌍 Intelligent Air Quality Monitoring & Disease Risk Assessment Platform ✨

A professional, interactive web application for real-time air quality and disease risk prediction, powered by machine learning and explainable AI. Visualize AQI and health risks on a map, search any city, and get actionable insights for public health awareness.

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🎥 Live Web App

➡️ Click to View the Web

Set the web application theme to 'light' for enhanced visibility

Air.Quality.and.Health.Risk.Prediction.Video.mp4

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🚀 Features

• 🗺️ Interactive Map: Click or search any location to view real-time air quality and weather data
• 📊 AQI Prediction: Predicts Air Quality Index using advanced ML models with 96.2% accuracy
• 🏥 Disease Risk Assessment: Estimates risk for multiple pollution-related diseases
• 🔍 Explainable AI: LIME/SHAP explanations for both AQI and disease predictions
• 💻 Modern UI: Clean, responsive, and professional design
• ⚡ Real-time Data: Integration with OpenWeather API for live environmental data
• 🔒 Secure: API keys secured via environment variables with input validation

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🛠️ Setup Instructions

Prerequisites

• Python 3.12 or higher
• pip package manager
• OpenWeather API key (free registration at OpenWeatherMap)

Installation

1. Clone the repository

   git clone https://github.com/Keerthishreekesavan/Intelligent-Air-Quality-Monitoring-Disease-Risk-Assessment-Platform-using-Hybrid-XAI

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables
   • Create a .env file in the root directory:

     OPENWEATHER_API_KEY=your_api_key_here

Run the Application

Main Application (Recommended):

streamlit run streamlit_map_app.py

Alternative Interface:

streamlit run streamlit_app.py

Open your browser and navigate to http://localhost:8501

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📦 Project Structure

Air_Pollution_Predictor/
├── streamlit_map_app.py      # Main interactive map application
├── streamlit_app.py          # Alternative interface
├── models.py                 # ML model definitions and utilities
├── requirements.txt          # Python dependencies
├── static/                   # Static assets
├── templates/                # HTML templates
├── *.pkl                     # Pre-trained ML models
├── *.csv                     # Example datasets
├── .env                      # Environment variables (create this)
└── README.md                 # This file

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📊 Models & Data

Machine Learning Models

• AQI Prediction Model: Random Forest/XGBoost with 96.2% accuracy
• Disease Risk Models: Ensemble methods with F1-score of 0.921
• Explainability: LIME and SHAP for model interpretability

Data Sources

• Real-time Environmental Data: OpenWeather API
• Historical Air Quality Data: Pre-processed datasets for training
• Health Statistics: Disease correlation data for risk assessment

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📈 Usage

1. Launch the application using the installation instructions above
2. Navigate to the map interface in your browser
3. Interact with the map:
   • Click on any location to get instant air quality data
   • Use the search bar to find specific cities
   • View real-time AQI values and predictions
   • Explore disease risk assessments for the selected location
   • Review model explanations to understand prediction factors

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📊 Performance Metrics

Metric	Value
AQI Prediction Accuracy	96.2%
Disease Risk Assessment F1	0.921
Average Response Time	<2 seconds
Model Inference Time	<100ms
API Rate Limit	1000/day

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🔒 Security Features

• API Key Security: Environment variables for sensitive data
• Input Validation: Comprehensive validation for all user inputs
• Rate Limiting: Built-in protection against API abuse
• Error Handling: Graceful handling of API failures and edge cases

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🌟 Key Technologies

• Backend: Python, Streamlit
• Machine Learning: scikit-learn, XGBoost, LIME, SHAP
• Data Visualization: Folium, Plotly, Streamlit
• APIs: OpenWeather API
• Deployment: Streamlit Cloud ready

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🚀 Future Enhancements

• 📱 Mobile app development
• 📈 Historical data visualization
• 🚨 Air quality alerts and notifications
• 🌐 Integration with more environmental APIs
• 🔮 Advanced forecasting models
• 🌍 Multi-language support

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🤝 Contributing

1. Fork the repository
2. Create your 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

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📄 License

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

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🙏 Acknowledgments

• Data Source: OpenWeather API for real-time environmental data
• ML Libraries: scikit-learn, XGBoost, LIME, SHAP for machine learning capabilities
• Visualization: Folium, Plotly, Streamlit for interactive visualizations
• Icons: Lucide React for UI components