How to Build a Real-Time Flood Prediction Visualizer

Simple Summary

A real-time flood prediction visualizer that empowers users with interactive maps and data-driven insights to anticipate and respond to potential flooding events.

Product Requirements Document (PRD)

Goals:

• Create a user-friendly platform for visualizing real-time flood predictions
• Provide accurate and timely flood risk information to users
• Enable customizable alerts and notifications for at-risk areas
• Improve community preparedness and response to flooding events

Target Audience:

• Emergency management officials
• Local government agencies
• Residents in flood-prone areas
• Insurance companies and risk assessors

Key Features:

1. Interactive map interface with real-time flood prediction overlays
2. Customizable alert system for user-defined locations
3. Historical flood data comparison and trend analysis
4. Integration with weather forecasts and river gauge data
5. User-friendly mobile app for on-the-go access
6. Admin dashboard for data management and system monitoring

User Requirements:

• Intuitive navigation and map interaction
• Clear, color-coded visualization of flood risk levels
• Ability to save locations and receive personalized alerts
• Access to educational resources on flood preparedness
• Responsive design for seamless use across devices

User Flows

1. New User Registration and Onboarding:

   • User downloads app or visits website
   • Creates account with email/password or social login
   • Completes brief onboarding tutorial
   • Sets up initial location preferences and alert settings

2. Checking Flood Predictions:

   • User logs in to the platform
   • Views interactive map with current flood predictions
   • Zooms/pans to area of interest
   • Toggles different data layers (e.g., precipitation, river levels)
   • Views detailed predictions for specific locations

3. Managing Alerts and Notifications:

   • User navigates to alert settings
   • Adds or removes locations for monitoring
   • Sets threshold levels for different alert types
   • Chooses preferred notification methods (e.g., push, email, SMS)
   • Tests notification system

Technical Specifications

Frontend:

• React for web application
• React Native for mobile app
• Mapbox GL JS for interactive maps
• D3.js for data visualization
• Redux for state management

Backend:

• Node.js with Express.js
• PostgreSQL with PostGIS extension for geospatial data
• Redis for caching and real-time updates
• Docker for containerization

APIs and Services:

• Weather API (e.g., OpenWeatherMap) for forecast data
• USGS Water Data API for river gauge information
• Mapbox API for base maps and geocoding

Machine Learning:

• Python with scikit-learn for predictive modeling
• TensorFlow for advanced flood prediction algorithms

DevOps:

• GitLab CI/CD for continuous integration and deployment
• AWS for cloud hosting and services

API Endpoints

• POST /api/auth/register
• POST /api/auth/login
• GET /api/flood-predictions
• GET /api/flood-predictions/{location}
• POST /api/alerts
• GET /api/alerts
• PUT /api/alerts/{id}
• DELETE /api/alerts/{id}
• GET /api/historical-data/{location}
• GET /api/weather-forecast/{location}

Database Schema

Users:

• id (PK)
• email
• password_hash
• name
• created_at
• updated_at

Locations:

• id (PK)
• user_id (FK)
• name
• latitude
• longitude
• created_at
• updated_at

Alerts:

• id (PK)
• user_id (FK)
• location_id (FK)
• type
• threshold
• created_at
• updated_at

FloodPredictions:

• id (PK)
• location_id (FK)
• prediction_time
• flood_level
• confidence
• created_at

File Structure

Implementation Plan

1. Project Setup (1 week)

   • Initialize repository and project structure
   • Set up development environments
   • Configure CI/CD pipeline

2. Backend Development (3 weeks)

   • Implement user authentication and authorization
   • Develop core API endpoints
   • Set up database and ORM
   • Integrate external APIs (weather, river data)

3. Frontend Development (4 weeks)

   • Create responsive UI components
   • Implement interactive map functionality
   • Develop data visualization features
   • Build user settings and alert management interfaces

4. Machine Learning Integration (2 weeks)

   • Develop and train flood prediction models
   • Create API for model predictions
   • Integrate ML predictions with backend services

5. Mobile App Development (3 weeks)

   • Port web app functionality to React Native
   • Implement mobile-specific features (e.g., push notifications)
   • Optimize performance for mobile devices

6. Testing and Quality Assurance (2 weeks)

   • Conduct unit and integration testing
   • Perform user acceptance testing
   • Address bugs and optimize performance

7. Deployment and Launch (1 week)

   • Set up production environment
   • Deploy application to cloud services
   • Conduct final checks and monitoring

8. Post-launch Support and Iteration (Ongoing)

   • Monitor system performance and user feedback
   • Implement feature improvements and bug fixes
   • Continuously update flood prediction models

Deployment Strategy

1. Use AWS as the primary cloud provider
2. Deploy backend services using AWS Elastic Beanstalk
3. Store data in Amazon RDS for PostgreSQL with PostGIS
4. Use Amazon ElastiCache for Redis caching
5. Deploy frontend to Amazon S3 with CloudFront for CDN
6. Implement AWS Lambda for serverless processing of flood prediction updates
7. Use Amazon SES for email notifications
8. Set up Amazon CloudWatch for monitoring and alerting
9. Implement blue-green deployment for zero-downtime updates
10. Use AWS WAF for additional security and DDoS protection

Design Rationale

The chosen tech stack (React, Node.js, PostgreSQL) provides a robust and scalable foundation for building a real-time flood prediction visualizer. React offers a component-based architecture for creating an interactive and responsive user interface, while Node.js enables efficient handling of real-time data streams. PostgreSQL with PostGIS extension is ideal for managing geospatial data required for flood predictions.

The use of Mapbox GL JS and D3.js allows for creating sophisticated, interactive visualizations that can handle large datasets smoothly. The mobile app built with React Native ensures a consistent user experience across platforms while maximizing code reuse.

The implementation plan prioritizes core functionality development before moving to advanced features and mobile app creation. This approach allows for early testing and iteration of the most critical components. The deployment strategy leverages AWS services to ensure scalability, reliability, and ease of management for a data-intensive application like a flood prediction visualizer.