Triple R Assessment
Project Overview
The Triple R Assessment project is designed to assess, prepare, and respond to potential crises or natural disasters. Using predictive models and data visualization, this project provides users with:
• Base Risk Score for specific locations and disaster types, calculated by custom machine learning model trained using FEMA data.
• Predictive Risk Score Analysis for future risk projections.
• Response Guidance with nearby resources like gas stations, stores, and evacuation cities.
• Recovery Guidance, including recovery-related YouTube video resources.
This project uses a combination of Python and JavaScript, leveraging Flask for the backend and Leaflet for interactive maps.
Project Structure
• app.py: Main backend server file running the Flask application.
• train_model.py: Python script to train the predictive model.
• PredictionDataSet.csv: Dataset for training and evaluating the predictive model.
• index.html: Frontend HTML template with embedded JavaScript for map-based visualization.
• requirements.txt: Lists required Python packages.
Features
1. Base Risk Score Calculation: Calculates risk based on disaster type, location, and existing data.
2. Predictive Analysis: Projects risk over a specified future period.
3. Interactive Maps: Displays essential resources and evacuation cities using Leaflet and Marker Clusters.
4. Guidance Tabs: Provides actionable response and recovery guidance based on location and disaster type.
Installation and Setup
Prerequisites
Ensure you have the following installed:
• Python 3.8+
• pip (Python package manager)
• Node.js and npm (for JavaScript package dependencies if you need additional customization)
Step-by-Step Setup
1. Clone the Repository:
git clone <https://github.com/AnvitD/TripleRRR.git>
cd TripleRRR
- Install Python Dependencies: Install the necessary Python packages listed in requirements.txt:
pip install -r requirements.txt
- Run the Flask Application: Start the application server:
python app.py
- Open the Application: Open your browser and go to http://127.0.0.1:5000 to view the Triple R Assessment web interface.
Dependencies
Backend (Python)
The backend uses the following Python libraries, specified in requirements.txt:
• Flask: For building the web server and handling HTTP requests.
• pandas: For data manipulation.
• numpy: For numerical computations.
• scikit-learn: For model training and predictive analysis.
• joblib: For saving/loading trained machine learning models.
Frontend (JavaScript and HTML)
The frontend relies on:
• Leaflet.js: For rendering interactive maps.
• Leaflet.MarkerCluster: For clustering map markers.
• Chart.js: For displaying predictive risk scores as line charts.
• FontAwesome: For icons used in the interface.
• Google Fonts: Specifically, the Roboto font for a clean and modern design.
Additional API Integration
The project utilizes APIs for map location data:
• Nominatim API: For obtaining latitude and longitude of user-selected locations.
• Overpass API: For querying nearby resources (e.g., gas stations, stores).
• YouTube Data API v3: For dynamically retrieving and displaying recovery-related YouTube videos based on the selected natural disaster type.
Usage Guide
• Navigate through the tabs to explore different functionalities: Recognition, Response, and Recovery.
• Input Location and Disaster Type in the Recognition tab to calculate the base risk score.
• Use Predictive Risk Slider to project risk scores for future periods.
• Map Resources in the Response tab show nearby resources.
• Search within the Response tab for evacuation cities using the search bar.
• Recovery Videos provide context and guidance for disaster recovery in the Recovery tab.
Future Improvements
• Extend predictive modeling capabilities with additional disaster types.
• Enhance the Response tab with real-time data from relevant government resources.
• Add additional customization for map display and markers.