AquaGuard
An AI-powered water quality monitoring platform that enables citizens to report unsafe water sources while helping governments and NGOs make faster, data-driven environmental decisions through machine learning.
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01 OVERVIEW
Inspired by the water scarcity challenges faced in Pakistan's Thar region, AquaGuard was developed to bridge the gap between communities and authorities. Citizens can report contaminated water with photos and location details, while NGOs and government officials upload laboratory measurements such as pH, DO, BOD, conductivity, and coliform levels. A machine learning model predicts whether the water is safe or unsafe, enabling faster response, greater transparency, and informed decision-making.
02 CHALLENGE
Limited Training Data
Reliable regional water-quality datasets were scarce, requiring extensive preprocessing and adaptation of publicly available datasets for machine learning.
AI Integration
Connecting a Python-based Random Forest prediction service with the Node.js backend while maintaining a smooth user experience.
Cloud Infrastructure
Deploying and integrating multiple AWS services including EC2, RDS, S3, and Cognito within the limited timeframe of a hackathon.
Accessible User Experience
Designing an intuitive platform suitable for citizens, NGOs, and government officials with different technical backgrounds.
03 SOLUTION
Developed a cloud-native platform that combines citizen-reported complaints, AWS infrastructure, secure authentication, image storage, and AI-powered water quality prediction to improve environmental transparency and accelerate response to unsafe water conditions.
04 FEATURES
05 TECH STACK
06 RESULTS
SUCCESSFUL
SCREENSHOTS