Inspiration
We wanted to build something that played to our strengths. Our team enjoys working with GIS, mapping systems, and large public data APIs, so HydroIQ gave us a way to combine spatial analysis, climate data, and financial data into a project with real-world value. We were especially motivated by the idea that water reuse is an overlooked sustainability opportunity, even though it can create clear environmental and financial benefits.
What it does
HydroIQ is an automated water reuse prospecting engine. It helps identify high-viability commercial and industrial buildings for rainwater harvesting and water reuse systems.
The platform analyzes building and roof data, combines it with rainfall and water-cost information, and calculates a viability score for each location. This makes it easier to find buildings where rainwater capture can deliver meaningful operational savings, resilience value, and sustainability impact.
How we built it
We built HydroIQ as a web-based platform using HTML, CSS, and JavaScript on the frontend, with a Java backend. For mapping and geospatial interaction, we used Mapbox and GIS-based data sources. We integrated building footprint and structure data, rainfall and climate data, and financial indicators like water costs and incentives.
We also used data processing tools like pandas to clean, organize, and transform raw datasets into something usable for our application. On top of that, we designed a scoring model that combines rainwater collection potential with water cost percentile to prioritize the strongest opportunities.
Challenges we ran into
The biggest challenge was finding good data and getting it into a usable form. We discovered a lot of useful sources, but they came in different formats, levels of detail, and structures, so a large part of our time went into cleaning, filtering, and manipulating the data before we could actually use it in the project.
Another challenge was connecting multiple systems together, including GIS data, map interaction, backend APIs, and financial logic, while keeping the user experience simple and intuitive.
Accomplishments that we're proud of
We are proud of how quickly we turned a complex idea into a working platform. One of our biggest accomplishments was learning how to use pandas effectively to clean and sort data much faster than we expected.
We are also proud that we built an interactive system that does more than just display information. HydroIQ actually helps surface valuable opportunities by combining building analysis, rainfall modeling, and financial scoring into one workflow.
What we learned
We learned a lot about working with geospatial data, climate datasets, and public APIs at scale. We also learned how important data cleaning is in any real-world project, especially when combining multiple outside sources.
On the technical side, we gained experience with Mapbox, backend API integration, and data transformation workflows. We also learned that a strong project is not just about having data, but about organizing it into a system that produces useful decisions.
What's next for HydroIQ
Next, we want to improve HydroIQ by adding machine learning to track and analyze real-time data. That would let the platform become more dynamic and more predictive over time.
We also want to expand the data pipeline, improve detection of promising building types, strengthen the financial model, and make the viability scoring even smarter. The long-term goal is to turn HydroIQ into a practical prospecting tool for water reuse providers and organizations looking to scale sustainability impact.
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