Google-Solution-23

🌍 Our Theme: ${\color{green}AQI \space : \space World's \space Climate \space Through \space Your \space Lens }$

• Introduction: In a world grappling with air pollution, understanding air quality is vital. The Air Quality Index (AQI) is a tool that provides insights into pollution levels and associated health risks.

• 🔍 Exploration of AQI Components: AQI measures various pollutants like particulate matter, ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. It explains their sources and health effects.

• 🧮 Calculation and Interpretation: The methodology behind AQI calculation, pollutant weighting, and category implications (from "Good" to "Hazardous") is essential for public health awareness.

• 🌐 Data Collection and Monitoring: Exploring AQI data sources, including government agencies, monitoring stations, and satellite observations, highlights the need for real-time monitoring and data transparency.

• 🌏 Global Perspectives: Comparing AQI systems worldwide provides insights into cultural, geographical, and regulatory factors affecting air quality management and international cooperation.

• 💪 Health Impacts and Mitigation Strategies: The health consequences of various AQI levels, along with effective mitigation strategies like emission controls and urban planning, underscore AQI's importance.

• 📢 Empowering Public Awareness: AQI plays a crucial role in raising public awareness about air pollution through educational initiatives, community engagement, and digital platforms.

• 📚 Case Studies and Future Outlook: City and regional success stories illustrate the impact of measures to improve air quality, offering insights into future trends in air quality management.

• 🔚 Conclusion: As we strive for clean air for future generations, AQI's role in fostering global collaboration and prioritizing public health is crucial for a healthier, more sustainable world.

🌱 SDGs We Addressed

• 11 Sustainable Cities and Communities
• 13 Climate Action
• 15 Life On Land

Sustainable Cities and Communities (11)

The Sustainable Development Goal (SDG) 11 aims to make cities and human settlements inclusive, safe, resilient, and sustainable. It targets issues such as access to adequate housing, sustainable transport, green spaces, and slum upgrading. The goal also emphasizes reducing the environmental impact of cities, including air pollution and waste management.

Climate Action (13)

SDG 13 focuses on urgent action to combat climate change and its impacts. It emphasizes strengthening resilience and adaptive capacity to climate-related disasters and integrating climate change measures into national policies and planning.

Life On Land (15)

SDG 15, Life on Land, aims to protect, restore, and promote sustainable use of terrestrial ecosystems, emphasizing biodiversity conservation and combating land degradation. Our project leverages image processing technology to monitor and assess the impact of human activities on terrestrial ecosystems, aligning with SDG 15's goals of sustainable land use and biodiversity preservation.

📊 Brief Approach

• Objective: Deploy optimized CNN models for image processing using Streamlit, fine-tuning with boosting algorithms, and specifying hyperparameters like Adam learning rate as 0.001.

• Steps:

  1. 📂 Dataset Preparation: Gather and preprocess a diverse image dataset.
  2. 🔍 Exploratory Data Analysis: Perform a deep analysis of the dataset to determine the model.

3. 🔧 Model Selection: Choose InceptionV3, RCNNs, and VGG16.
4. 🛠️ Model Training: Implement and train models, fine-tuning with boosting algorithms.
5. 📈 Performance Evaluation: Assess model performance using standard metrics.
6. 🚀 Deployment with Streamlit: Create a user-friendly web app interface.
7. 🔗 Integration: Deploy fine-tuned models within the Streamlit app.
8. 🧪 Testing and Validation: Ensure functionality and performance meet expectations.

9. 📝 Documentation: Document the process, results, and recommendations.

🛠️ Technologies Used:

1. TensorFlow
2. Keras
3. NumPy
4. Pandas
5. Matplotlib
6. Seaborn
7. Streamlit
8. VGG16
9. InceptionV3
10. Boosting Algorithms
11. OpenCV
12. Pillow

This approach ensures efficient deployment of optimized CNN models, facilitating seamless image processing tasks for end-users.

📚 Information about the Approach

• 🌫️ Air Pollution: High air pollution levels, indicated by elevated Air Quality Index (AQI), can adversely affect vegetation, including lichens. Lichens are particularly sensitive to air quality changes. Increased pollutants like sulfur dioxide and nitrogen oxides can harm lichens, leading to reduced diversity and abundance. Monitoring AQI is crucial for understanding and mitigating the impact of air pollution on ecosystems.

• 🖼️ VGG16:

  1. Architecture: VGG16's uniform architecture, with 3x3 filters in convolutional layers and 2x2 pooling layers, stands out for its simplicity and clarity.
  2. Depth: While relatively shallow compared to modern architectures, VGG16's 16 layers contributed to its computational cost and effectiveness.
  3. Performance: VGG16 achieved state-of-the-art results on ImageNet, demonstrating the power of its design and depth.
  4. Legacy: VGG16 has influenced subsequent architectures and remains a reference point in understanding convolutional neural network design.

📦 Resources

• Dataset used: Air Pollution Image Dataset from India and Nepal. Kaggle. Link to Dataset

🧑‍🤝‍🧑 Contributors

• Anidipta Pal
• Ankana Datta
• Anjishnu Mukherjee
• Ananyo Dasgupta