Intelligent Water Decision Support Platform (AquaSentinel)

Inspiration

Clean water is essential, yet many rivers and lakes are affected by pollution. Water quality monitoring is often manual, time-consuming, and difficult to scale. We wanted to create a platform that combines IoT sensing and AI to help authorities make faster, data-driven decisions. AquaSentinel was inspired by the need for an intelligent, affordable, and scalable solution for monitoring water quality and supporting environmental protection.

What it does

AquaSentinel is an AI-powered Intelligent Water Decision Support Platform. The platform collects physicochemical water quality parameters (pH, TDS, turbidity, and water temperature), calculates a Water Quality Score (WQS) based on WHO guidelines, and uses AI to generate environmental risk assessments, possible causes, health notes, and actionable recommendations. It also supports monitoring multiple water bodies through a centralized Water Intelligence Network, allowing authorities to compare locations, analyze trends, and prioritize environmental action.

How we built it

We built AquaSentinel using: ESP32-based IoT hardware for collecting water quality data Water quality sensors (pH, TDS, Turbidity, Temperature) HTML, CSS and JavaScript for the web platform Groq AI for environmental analysis and decision support Supabase as the cloud database for storing monitoring data Netlify for deployment The platform combines sensor data, cloud storage, AI analysis, and interactive dashboards into a single decision-support system.

Challenges we ran into

One of our biggest challenges was designing a scalable architecture while working with only a single hardware prototype. We also had to: Integrate AI-generated environmental insights. Design an intuitive dashboard that is easy for anyone to understand. Build a realistic multi-station monitoring system without deploying hardware at every location. Optimize the platform to work within free cloud service limits while maintaining a professional user experience.

Accomplishments that we're proud of

Built a complete end-to-end Intelligent Water Decision Support Platform. Successfully integrated IoT sensing, cloud storage, AI analysis, and visualization. Developed an AI Water Risk Predictor that generates meaningful environmental insights. Designed a scalable architecture capable of supporting multiple monitoring stations. Created a modern, responsive interface suitable for demonstrations and future real-world deployment.

What we learned

This project taught us much more than programming. We learned about: IoT system integration Environmental monitoring standards Water quality assessment using WHO guidelines Cloud databases and REST APIs AI prompt engineering Designing scalable software architectures Building user-friendly interfaces for technical systems

What's next for Intelligent Water Decision Support Platform (AquaSentinel)

Our future roadmap includes: Historical trend and pattern analysis using long-term monitoring data. Citizen Reporting Portal where users can submit pollution reports with text and images. AI-generated complaint reports with direct links to the appropriate State Pollution Control Board. Automatic anomaly detection and early pollution alerts. Live deployment across multiple rivers, lakes, and reservoirs. Integration with weather data and predictive environmental analytics. Expansion into a city-wide and eventually nationwide intelligent water monitoring network

Built With

• ai
• api
• cloud
• css3
• data
• design
• environmental
• esp32
• groq
• html5
• iot
• javascript
• monitoring
• netlify
• quality
• responsive
• rest
• supabase
• visualization
• water