Inspiration

Water is very important for both people and the environment, but many people don't really understand what water quality data means. We were inspired by the idea of making water monitoring more simple and useful for normal users.

We wanted to build something where water related data can be analysed and converted into information that people can understand easily. This idea led us to build AquaSense AI, with a focus on connecting water ecosystem health with human well-being.

What it does

AquaSense AI is an AI based platform for analysing and understanding water quality data.

It takes water related parameters and uses AI/data analysis to understand the condition of the water. Instead of only showing technical numbers, the system tries to present the information in a simpler way so users can understand possible water quality problems.

The main goal is to help citizens, researchers and communities understand water conditions and make better decisions based on the available data.

It also follows the One Health approach by connecting the health of freshwater ecosystems with human health and community well-being.

How we built it

We built AquaSense AI by combining a user friendly interface with AI and data analysis.

The basic workflow is simple: water related data is provided to the system, the data is processed and analysed, and the results are presented to the user in an understandable format.

We focused on making the system simple enough for people who may not have a strong background in environmental science. The project was developed as a working prototype which can also be extended in the future with real-time sensor data, citizen science observations and historical water data.

Challenges we ran into

One of the main challenges was understanding how different water parameters can be used to determine the condition of water.

Another challenge was making technical environmental data easy to understand. We did not want the user to just see a lot of numbers without knowing what they mean.

We also had to think about how AI should be used responsibly. AI should support human decision making rather than completely replace experts, especially when dealing with environmental and health related information.

Building and testing the complete prototype within the available time was also challenging, so we focused mainly on the core features and kept the system flexible for future improvements.

Accomplishments that we're proud of

We are proud that we were able to turn the idea of AI based water analysis into a working prototype.

We also managed to connect the technical side of AI and data analysis with a real environmental problem.

Another thing we are proud of is the focus on simplicity. Our goal was not just to create another dashboard, but to make water quality information easier for people to understand and use.

Most importantly, AquaSense AI gave us an opportunity to work on a problem where technology can potentially help both the environment and communities.

What we learned

While building AquaSense AI, we learned that solving a real-world problem is not only about writing code. Understanding the actual problem and the users is equally important.

We learned more about water quality data, AI based analysis and how environmental information can be converted into useful insights.

We also learned how important it is to keep AI systems explainable and make sure that AI supports human decisions instead of blindly replacing them.

Working on the project also improved our experience in developing a complete prototype within a limited time.

What's next for AquaSense AI

There is a lot of scope for improving AquaSense AI in the future.

We want to connect it with IoT based water sensors so that water data can be collected automatically in real time.

We also want to add citizen science features where people can contribute observations about local water bodies.

In the future, historical data can be combined with AI models to detect trends and provide early warnings when unusual changes in water quality are detected.

We also want to add maps, better visualizations and support for multiple water bodies so AquaSense AI can become a more complete water ecosystem monitoring platform.

Our long term goal is to make AquaSense AI a system that can turn environmental data into useful and understandable information for healthier water ecosystems and healthier communities.