California Ecosystem Resilience Simulator

An interactive environmental simulation platform that uses machine learning and natural language processing to model and visualize ecosystem changes in California.

🌟 Features

• Natural Language Interface: Interpret environmental change requests using LLM
• Real-time Simulation: Model environmental impacts across multiple factors
• Interactive Visualization: Dynamic charts and metrics for impact analysis
• Geographical Integration: County-level environmental data analysis
• Confidence Scoring: Reliability metrics for predictions

🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 14+
• MongoDB Atlas account
• Google Cloud (Vertex AI) account

Backend Setup

1. Create and activate virtual environment:

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

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env
# Edit .env with your credentials:
# - MONGO_URI
# - AZURE_STORAGE_CONNECTION_STRING
# - GOOGLE_CLOUD_PROJECT

4. Run the backend:

cd backend
python app.py

Frontend Setup

1. Install dependencies:

cd frontend
npm install

2. Run the development server:

npm run dev

📁 Project Structure

backend/
├── ml/
│   ├── models/           # ML models and inference
│   ├── analyze_merged_data.py
│   └── convertpis.py    # Data preprocessing
├── api/
│   └── environment_routes.py
└── app.py               # Main Flask application

frontend/
├── src/
│   ├── pages/          # React components
│   └── components/
└── app/
    └── layout.tsx      # Root layout

🔧 Key Components

Machine Learning Pipeline

• Bayesian Network for environmental modeling
• LLM for natural language understanding
• Geographical data integration
• Confidence scoring system

Visualization System

• Environmental changes tracking
• Impact analysis
• Confidence metrics
• Distribution analysis

API Endpoints

• /api/simulate: Run environmental simulations
• /api/messages: Process natural language inputs
• /api/variables: Get available environmental variables

📊 Data Sources

The simulator uses various California environmental datasets:

• CalEnviroScreen 3.0
• Species Biodiversity Data
• Geographical/County-level Data
• Climate Vulnerability Metrics

🧪 Testing

Run the test suite:

cd backend/ml/models
python test_llm_pipeline.py

View test results and visualizations in:

• llm_pipeline_results.png
• llm_pipeline_results.pdf
• llm_pipeline_test_results.json

🌿 Environmental Variables

Key environmental factors modeled:

• Air Quality (PM2.5, Ozone)
• Traffic Patterns
• Biodiversity Metrics
• Species Vulnerability
• Pollution Burden
• Habitat Quality

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Push to the branch
5. Open a Pull Request

📝 Notes

• Visualizations are saved in the backend/ml/models directory
• Geographical data integration requires proper GeoJSON files
• Some features require specific API access (Vertex AI, MongoDB)

⚠️ Known Issues

1. Frontend 3D models need separate installation
2. Some biodiversity metrics need fine-tuning
3. Geographical data sources need to be configured

📜 License

MIT License - see LICENSE file for details