ClimateTwin-AI

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  Complete-Overview

Inspiration

Europe is experiencing record-breaking heatwaves, rising temperatures, and more frequent climate-related disasters caused by global warming. We wanted to build a tool that helps people understand how today's environmental decisions could influence tomorrow's climate and support smarter, data-driven climate action.

What it does

ClimateTwin AI is an AI-powered climate simulation platform that lets users explore "what-if" scenarios and see how actions like planting trees, reducing emissions, or adopting renewable energy could affect climate indicators over the next 5–10 years.

How we built it

We built ClimateTwin AI using React, FastAPI, Python, Scikit-learn, Leaflet, and PostgreSQL, combining historical climate data with AI-driven scenario projections and interactive visualizations.

Challenges we ran into

Our biggest challenges were designing realistic climate scenarios, integrating multiple environmental datasets, and presenting complex AI projections in a simple, understandable way.

Accomplishments that we're proud of

We created a working platform that transforms climate data into interactive future scenarios, helping users visualize the potential impact of their environmental choices through AI-powered insights.

What we learned

We learned how to combine AI, climate data, forecasting, and intuitive visualization to build a decision-support tool focused on sustainability and real-world impact.

What's next for ClimateTwin AI

We plan to integrate real-time satellite and weather data, improve projection models, support city-level digital twins, expand global coverage, and provide policymakers with more advanced climate planning tools.

Built With

• alembic
• css
• docker
• fastapi
• groq
• html
• leaflet.js
• pandas
• postgresql
• prophet
• pydantic
• python
• react
• recharts
• scikit-learn
• sqlalchemy
• tailwind
• xgboost