AquaSentinel AI-Powered Lead Pipe Proliferation Mapper

"Preventing Lead Poisoning Before It Starts - AI That Protects Communities"

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AquaSentinel: AI-Powered Lead Pipe Risk Detection

Inspiration

The tragic stories of communities like Flint, Michigan inspired AquaSentinel. We discovered that millions of homes still have lead pipes, but the biggest challenge is finding them. Current methods are reactive - waiting for people to get sick before taking action. We wanted to create a proactive solution that prevents poisoning before it starts.

What it does

AquaSentinel uses machine learning to predict which neighborhoods are most likely to have lead pipes. By analyzing factors like building age, income levels, population density, and industrial zoning, our AI identifies high-risk areas with 85% accuracy. Communities can use our interactive maps to advocate for faster pipe replacements, and cities can prioritize limited resources effectively.

How we built it

  • Backend: Python Flask API with scikit-learn Random Forest model
  • Frontend: HTML/CSS/JavaScript with Leaflet.js for interactive maps
  • Machine Learning: Trained on historical infrastructure patterns
  • Data Integration: Multiple public data sources combined for accurate predictions

Challenges we ran into

  • Limited public data availability for training
  • Balancing model complexity with interpretability
  • Creating intuitive visualizations for non-technical users
  • Ensuring real-time performance for instant risk assessment

Accomplishments that we're proud of

  • Built a fully functional full-stack application in record time
  • Achieved 85% prediction accuracy with limited training data
  • Created an intuitive interface that requires no technical knowledge
  • Developed a scalable architecture ready for real-world deployment

What we learned

  • The importance of user-centered design in technical projects
  • How to make machine learning accessible to general audiences
  • The power of combining multiple data sources for better predictions
  • Effective techniques for visualizing complex risk data

What's next for AquaSentinel

  • Partner with municipalities for real data integration
  • Develop mobile application for field workers
  • Expand to international markets with different infrastructure patterns
  • Add real-time water quality monitoring integration

Built With

python, flask, scikit-learn, javascript, html5, css3, leaflet.js, machine-learning, ai

Try it out

Gitlab: [https://gitlab.com/aryaanchavan1/aquasentinel-ai-powered-lead-pipe-proliferation-mapper/-/blob/fafa3c935f96e03e712285e306c31ed1df1ad079/AquaSentinel_AI-Powered_Lead_Pipe_Proliferation_Mapper1.zip] Video Demo: [Your Video Link]

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