MeghPath
Computer vision continuously monitors drainage inlets, detects waste, and removes blockage risks before waterlogging builds.
VarunNetra, meaning "Water's Eye", is an integrated framework that brings AI vision, IoT sensing, and sustainable design into one connected urban water intelligence system. Built by two students from Haryana.
Unlike fragmented approaches that tackle drainage, supply, or harvesting in isolation, VarunNetra unifies them into a closed-loop ecosystem that is predictive, responsive, and scalable.
Each module solves a different water challenge, but the real strength is how they work together as one closed-loop system.
Computer vision continuously monitors drainage inlets, detects waste, and removes blockage risks before waterlogging builds.
Embeds pressure and pH sensors in pipelines, sending live readings through NodeMCU to a Python dashboard for anomaly detection.
Captures rainwater vertically, filters through gravel, sand, and activated carbon, and stores it for non-potable reuse while supporting birds.
Early prototype results show why integrated water intelligence outperforms isolated single-problem solutions.
Drain blockage reduction across plastic, paper, and organic debris tests through automated response.
Estimated reduction in urban flooding risk from better drainage continuity and fewer reactive cycles.
Water loss reduction through leak and illegal-tapping detection using pressure anomalies and pH deviations.
Freshwater demand reduction through local rainwater reuse for non-potable applications.
Maximum response time to water-system issues — significantly faster than manual inspection cycles.
Integrated AI + IoT + Rainwater system in one closed-loop framework instead of siloed solutions.
Three physical modules feed one intelligence layer, while citizen participation closes the loop.
VarunNetra was recognized as a National Winner, validating the technical merit and real-world applicability.
India submission for SJWP 2026, one of the world's most respected youth water innovation platforms.
Prototype proves the concept. Next phase: real urban testing, stronger AI datasets, and cost optimization for municipal deployment.
High school student from Haryana with strong interest in robotics, AI systems, and scientific research. Leads machine learning and system integration for VarunNetra.
Focused on IoT, sustainable infrastructure, and interdisciplinary innovation. Designed the Jal Setu sensor array and the Jal Stambh harvesting structure.
Report water and drainage issues, track community activity, and help authorities respond with better local visibility.
Your report is logged with location details for clearer context and follow-up.
The more precise your location and details, the more useful the report becomes.
Share local water issues and build visible community context around recurring problems.
Share on-the-ground context and recurring local issues.
Recent public posts help build a stronger picture of water and drainage issues.
Public forum posts are plotted on the satellite map so you can see where community water issues are being reported.