Accomplishments that we're proud of
We were able to embed a React app into our Streamlit dashboard to visualise the training process and it worked wonderfully. It blended perfectly with the dashboard and caused no issues. We were also proud of the fact that we were able to use the GPU compute capabilities of RCAC for training the GNN which was superbly fast and allowed us to improve the model and readily test it rather than being bottlenecked by limited compute power in a 36 hour hackathon.
One of the most incredible achievements was to classify all the regions with Koppen-Geiger classifications. It allowed us to see climate shift until 2100, and that was extremely insightful and informed us with what is wrong. The next powerful implementation was to add the Koppen-Geiger classification to the GNN. We wanted to asses and truly come up reasonable estimates for the model, and upon integrating Koppen-Geiger into the GNN, we found the model optimization was significantly better.
Lastly, we were proud to have semantic search embedded successfully to find simulations.
We built a user-centric webpage that allows a non-expert to model climate-risks, simulate interventions and analyse visual evidence to take an informed decision for a better tomorrow.
What we learned
One of the takeaways was learning how to use Gemini embeddings to search through generated video simulations. This was one of the most important steps towards making SenTree more accessible to a lay audience. Instead of forcing users to manually browse many generated outputs, we were able to make the results easier to query and connect the modelling pipeline to a more intuitive discovery experience.
However, the most important key takeaway was to look at climate shift in real time and find out solutions that worked the best for the regions. We realized, although, climate change is an increasingly crucial problem to solve, if we widen our solution implementations to include predicted weather forecasting, risks, and better interventions, we can make a significant impact.
We also learnt how to adjust and tune weights using Koppen Geiger in a GNN to improve performance. Working through the training behaviour and carefully looking at the loss curves gave us a better understanding of what choices we can make that would positively affect risk propagation, prediction quality, and overall model usefulness.
What's next for SenTree
The next steps for SenTree include implementing financial research models and combine stocks data, to not just invest in regions and interventions, but also companies. This widens our audience from just big organizations to empathetic environment-conscious individuals.
Moreover, we hopefully, would like to integrate this software with a real organization and quantify their impact from using decisions from this app, and integrate more useful and applicable data to the app.
Finally, out of curiosity, we want to explore more about climate shifts and what are the prominent features causing these shifts.
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