SaravanavelE

SaravanavelE / Real-Time-Urban-Air-Quality-Intelligence-Alert-System

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Real-Time Urban Air Quality Intelligence & Alert System is a fully streaming, AI-driven air quality monitoring system that ingests sensor data from urban pollution stations across Indian cities, processes it in real-time using the Pathway framework, applies a trained Random Forest classifier to assess health risk levels (LOW / MEDIUM / HIGH).

19
0
100% credibility
Found Feb 27, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project creates a demonstration system for real-time monitoring of urban air quality using simulated sensor data, AI-driven risk assessments, a live dashboard, and web access points for alerts and predictions.

How It Works

1
🌿 Discover GreenPulse AI

You hear about this friendly tool that watches city air like a guardian, spotting pollution dangers live and warning you right away.

2
💻 Get it ready on your computer

Download the files and follow simple steps to set everything up, just like installing a new app.

3
▶️ Turn on live monitoring

Hit the start button, and it begins pulling in pretend sensor readings from busy cities like Delhi and Mumbai, feeling the buzz of real-time action.

4
📊 Open the colorful dashboard

Visit the easy web page where gauges and charts light up with current air quality scores and trends across cities.

5
🚨 Spot your first smart alert

Watch the smart brain analyze the numbers and pop up warnings like 'High risk!' with details on what's bad in the air.

6
🔗 Check latest updates or predict

Peek at fresh readings or test what-if scenarios for any city's air data through simple web links.

🌟 Breathe easier, informed

Now you have your own live air quality watchdog, keeping you safe and aware of urban pollution risks anytime.

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Star Growth

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AI-Generated Review

What is Real-Time-Urban-Air-Quality-Intelligence-Alert-System?

This Python project delivers a real-time urban air quality monitoring system that pulls streaming sensor data from Indian cities, computes CPCB-standard AQI scores from pollutants like PM2.5 and NO2, and applies a Random Forest classifier to flag health risks as LOW, MEDIUM, or HIGH. Users get a live FastAPI REST API for querying latest readings, alerts, and on-demand predictions, plus a Streamlit dashboard showing gauges, trends, and real-time feeds. It solves the gap in batch-only monitors by enabling predictive alerts via Pathway's streaming engine.

Why is it gaining traction?

It stands out as a turnkey real-time dashboard on GitHub, with one-command startup that spins up data simulation, processing, API, and viz—no manual orchestration. Developers dig the reactive API endpoints like /api/v1/latest and /alerts for instant urban monitoring, plus easy extension to real sensors or Kafka. The hook is prototyping AI-driven real-time urban systems, from air quality to flood forecasting, without building streaming from scratch.

Who should use this?

Data engineers exploring Pathway for real-time pipelines, IoT devs simulating urban sensor networks like pollution stations or traffic probes, and civic hackers building dashboards for metro air quality or microclimate analysis. Perfect for teams needing a FastAPI backend with Streamlit frontend for real-time detection projects.

Verdict

Grab it as a low-risk starter for real-time urban monitoring demos—strong user-facing API and dashboard, solid docs—but 17 stars and 1.0% credibility signal early maturity with minimal tests. Fork and harden for production use cases.

(198 words)

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