AquaGaurd AI: One Photo Could Help Protect Our Water. But Who Fixes the Problem?
What if reporting contaminated drinking water or sewage entering a pond took just one photo? Discover how AquaGaurd AI combines AI-powered image analysis, GPS location tracking, and AWS cloud infrastructure to help communities report suspected water pollution and connect incidents with the right authorities.
AquaGaurd AI: From Contaminated Water to Actionable Reports
Imagine waking up one morning and noticing that the drinking water in your area looks unusual. Perhaps sewage is flowing into a nearby pond, or the water in a village well appears dirty.
You want to report it. But whom should you contact? How do you explain the exact location? And what happens after the complaint is submitted?
A few days later, the same problem may still be there.
The water is still polluted. The complaint may be closed. But has the problem actually been fixed?
That question inspired our idea for AquaGaurd AI.
What if reporting a suspected water problem took just one photo, while AI helped classify the visible issue and GPS helped identify its location?
The Problem: When Water Safety Becomes Everyone's Concern
Access to clean water is essential, yet communities can face problems such as suspected drinking-water contamination, sewage entering wells and ponds, wastewater leakage, and visible pollution in local water bodies.
Reporting these incidents can be difficult when citizens do not know which department is responsible or how to communicate the exact location.
Authorities also need an organized way to review complaints, identify affected areas, and track the progress of reported incidents.
We wanted to make this process simpler by connecting citizen reports with location information and an organized complaint-management workflow.
Our Idea: One Photo. One Location. A Clearer Path to Action.
AquaGaurd AI is an AI-assisted, GPS-enabled water incident reporting platform designed to help citizens report suspected water pollution through a web application.
Citizens can capture or upload a photograph of a suspected problem. The system analyzes the image using a pre-trained computer vision model, records the reported location when permission is available, and helps prepare a complaint for the appropriate department.
The goal is simple:
Capture the issue. Locate the problem. Report it. Track the response.
1. AI-Assisted Water Incident Identification
Not everyone knows how to classify a water-related issue or describe it using technical terms.
AquaGaurd AI integrates a pre-trained Roboflow computer vision model to analyze uploaded photographs and return predictions for the visual categories it supports.
For example, photographs may show visible wastewater discharge, sewage overflow, or other signs of pollution. The model's actual capabilities depend on its training data and supported classes.
The predicted category can help organize the complaint and suggest the relevant department.
However, an image cannot reliably determine whether water contains harmful bacteria, viruses, heavy metals, or other invisible contaminants. AI predictions are preliminary observations, and suspected drinking-water contamination requires appropriate water-quality testing.
2. GPS-Based Reporting and Complaint Registration
When a citizen reports an incident, AquaGaurd AI can obtain geographical coordinates with the user's permission.
The citizen can review the location and confirm the complaint details before submission.
The complaint record can include:
- A unique complaint ID.
- The uploaded photograph.
- The reported issue category.
- GPS coordinates and available location details.
- The assigned department and complaint status.
This helps connect a photograph of a suspected water problem with the location where it was reported.
3. From Citizen Reports to an Authority Dashboard
AquaGaurd AI brings complaint information together in a dashboard designed for authorized administrators and department officers.
An interactive map can display reported incidents at their recorded locations. Officers can review the available information, inspect the image, and update the complaint status as the issue progresses.
The system can also help organize reports by category, department, and status.
Over time, this location-based information could help authorities identify areas with repeated reports and prioritize inspections.
Recurring-incident analysis and verified repair tracking remain important opportunities for further development.
How We Built AquaGaurd AI on AWS
Our application architecture uses AWS cloud infrastructure to support frontend hosting, backend execution, database connectivity, image storage, and network security.
1. Amazon ECR — Container Image Storage
Amazon Elastic Container Registry (ECR) stores the backend Docker image so that Amazon ECS can retrieve the image for deployment.
- Repository:
aquaguard-backend
- Image tag:
v1
2. Amazon ECS with AWS Fargate — Backend Deployment
Amazon ECS with AWS Fargate runs the containerized Node.js and Express backend without requiring us to manage the underlying EC2 servers directly.
- Cluster:
aquaguard-cluster
- Service:
aquaguard-backend-service
3. Amazon RDS for PostgreSQL — Database
Amazon RDS for PostgreSQL provides persistent database storage for application records, including reported incidents and complaint details.
- Database:
aquaguard_db
4. Amazon S3 — Website Hosting
Amazon S3 stores the frontend website files, including HTML, CSS, JavaScript, and other static assets.
- Bucket:
aquaguard-ai-website
5. Application Load Balancer — Request Routing
An Application Load Balancer routes incoming HTTP requests to the backend service and performs health checks on its targets.
- Load balancer:
aquaguard-alb
- Target group:
aquaguard-backend-tg
6. AWS Secrets Manager — Credential Management
AWS Secrets Manager stores database credentials separately from application code, helping reduce the risk of exposing sensitive configuration values.
7. Amazon VPC and Security Groups — Network Security
Amazon VPC provides the networking environment for the application components, while security groups control permitted traffic between the load balancer, backend, and database.
Together, these services support the infrastructure required to host the application, execute the backend, persist complaint records, and manage application traffic.
What Makes AquaGaurd AI Matter?
For citizens: Reporting a suspected water problem can become simpler through photo-based submission and location capture.
For authorities: Organized incident records and map-based visualization can help officers review reports and understand where problems have been reported.
For communities: A more accessible reporting process can help bring attention to suspected water pollution and recurring local concerns.
Our current focus is on water incident reporting and complaint management. Detecting recurring incidents, verifying repairs with before-and-after evidence, and confirming resolution with citizens are areas for future development.
What We Still Need to Improve
AquaGaurd AI needs real-world evaluation with citizens and local authorities.
The AI model must be tested against representative photographs, GPS accuracy must be evaluated, and the complaint workflow must be validated under realistic conditions.
Visible pollution does not always reveal the true quality of water. Appropriate laboratory testing and field inspections remain essential for confirming contamination.
Integration with official municipal complaint systems would also require authorization and cooperation from the relevant authorities.
These steps are important for turning a working prototype into a dependable environmental reporting platform.
The Bigger Picture
A photograph can document a visible water problem. GPS can help locate the reported incident. AI can assist with categorization, and cloud infrastructure can support the reporting workflow.
AquaGaurd AI brings these capabilities together to make suspected water-pollution reporting more accessible and help authorities review incidents more effectively.
We believe that improving how environmental problems are reported is a meaningful step toward better-informed water management.
Because protecting water is not only about identifying a problem. It is also about making sure the problem receives the attention it deserves.
AquaGaurd AI — Capture It. Locate It. Report It. Protect Our Water.
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