Climate Decision Support System

Real-time data analysis platform for climate and water management across 30 cities

Overview

Developed a comprehensive real-time decision support system during my MITACS Globalink Research Internship at Polytechnique de Montréal. This system processes and analyzes 20,000+ climate and water datasets across 30 cities, providing actionable insights for infrastructure planning and resource management.

Research Impact

  • 25% improvement in forecast accuracy through advanced ML models
  • 30% increase in planning efficiency for infrastructure development
  • Real-time processing of massive environmental datasets
  • Scalable architecture supporting multiple cities simultaneously

Technical Architecture

Frontend Development

  • React.js for responsive, interactive user interface
  • Real-time data visualization with dynamic charts and graphs
  • Multi-city dashboard for comparative analysis
  • Mobile-responsive design for field accessibility

Backend Infrastructure

  • Node.js for high-performance server-side processing
  • RESTful APIs for data access and system integration
  • Real-time data streaming for live updates
  • Microservices architecture for scalability

Machine Learning Pipeline

  • Advanced ML models for climate prediction and analysis
  • Time-series forecasting for water resource management
  • Anomaly detection for early warning systems
  • Optimization algorithms for resource allocation

Key Features

Data Processing

  • Multi-source integration: Combines satellite data, weather stations, and IoT sensors
  • Real-time analytics: Processes streaming data with minimal latency
  • Data quality assurance: Automated validation and cleaning pipelines
  • Historical analysis: Long-term trend analysis and pattern recognition

Decision Support

  • Predictive modeling: Forecasts climate conditions and water availability
  • Risk assessment: Identifies potential infrastructure vulnerabilities
  • Resource optimization: Recommends efficient allocation strategies
  • Alert systems: Early warning for extreme weather events

Technologies & Infrastructure

  • Frontend: React.js, JavaScript, HTML5, CSS3
  • Backend: Node.js, Express.js, REST APIs
  • Database: MongoDB for document storage, Redis for caching
  • ML/AI: Python, scikit-learn, TensorFlow for predictive models
  • Infrastructure: Linux servers, Docker containers, AWS cloud services
  • Data Sources: Climate APIs, satellite imagery, IoT sensor networks

Research Environment

Conducted at Polytechnique de Montréal as part of the prestigious MITACS Globalink Research Internship program. The project involved collaboration with leading researchers in environmental engineering and climate science, working in state-of-the-art research facilities.

Impact & Applications

Urban Planning

  • Infrastructure development: Informed decision-making for city planning
  • Resource management: Optimized water distribution and storage
  • Climate adaptation: Prepared cities for changing environmental conditions

Research Contributions

  • Methodology development: Novel approaches to multi-city climate analysis
  • Open-source tools: Contributed to the broader research community
  • Publication potential: Results suitable for top-tier environmental journals

Future Enhancements

  • AI-powered recommendations: Advanced machine learning for automated decision support
  • Global expansion: Scaling to additional cities and regions
  • Integration capabilities: APIs for third-party system integration
  • Mobile applications: Field-ready tools for environmental monitoring

Repository

The complete source code and documentation are available on GitHub, including detailed setup instructions and API documentation for researchers and developers interested in building similar systems.