RiverLens

Mapping the pulse of India's water

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Inspiration

Living in Uttar Pradesh, I witnessed a disconnect between the visible pollution in our rivers and the complex, spreadsheet-heavy data provided by the government. Most citizens know there is a crisis, but they don't have the tools to quantify it. RiverLens was born from a desire to turn "invisible" chemical data into a transparent, visual "Lens" that anyone can use to hold environmental standards accountable.

What it does

RiverLens is a high-fidelity GIS dashboard that monitors the health of India’s major river systems.

Interactive Mapping: Uses a satellite-hybrid interface to track 50+ rivers across the country.

A–F Grading System: Translates complex parameters like BOD (Biochemical Oxygen Demand) and Dissolved Oxygen into simple, color-coded health grades.

AI Insights: Integrates Llama-3 via Groq to provide plain-English explanations of what the data means for local communities, moving beyond raw numbers into real-world impact.

How we built it

The project is built on a modern, high-performance tech stack:

Frontend: React.js with Tailwind CSS for a professional "Official Dashboard" aesthetic.

Mapping: Leaflet.js with custom GeoJSON overlays for legally accurate Indian administrative boundaries.

AI Engine: Groq Cloud (Llama-3-70B) for near-instant inference and data interpretation.

Deployment: Vercel for the live CI/CD pipeline.

Data Logic: A custom JavaScript weighted-algorithm that parses 2021-2025 CPCB datasets to determine environmental health scores.

Challenges we ran into

Data Normalization: Cleaning and standardizing inconsistent government data (e.g., handling "Partial Data" flags) was a major hurdle.

GIS Performance: Managing high-resolution satellite tiles and pulsing markers simultaneously without causing lag on mobile devices.

PowerShell vs. Bash: As a Windows user, I had to overcome environment-specific terminal errors when trying to run global rebranding scripts across the codebase!

Accomplishments that we're proud of

Live Deployment: Successfully shipping a fully functional app to river-lens.vercel.app.

UI/UX Polish: Creating a "Dark Ink" professional theme that feels like a legitimate scientific tool.

The AI Bridge: Successfully using an LLM to act as a "Scientific Translator," making BOD/DO levels understandable for non-scientists.

What we learned

Through this project, I gained a deep understanding of Geospatial coordinates, the physics of water oxygenation, and the nuances of React state management when syncing a sidebar with a map. I also learned that "Good Design" is just as important as "Good Data"—if people can't navigate the map, the data doesn't matter.

What's next for RiverLens

Crowdsourced Verification: A feature allowing users to upload geo-tagged photos of river discharge to verify the data in real-time.

Predictive Analysis: Using historical trends to forecast river health for 2027 and beyond.

Regional Deep-Dives: Creating hyper-local views for specific districts, starting with the rivers of Uttar Pradesh.

Built With

  • framer-motion
  • leaflet.js
  • llama-3-(groq-api)
  • lucide-react
  • react
  • tailwind-css
  • vercel
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