AI for Environmental Decision Intelligence - The AI Forecasting Hackathon a
Aleena Sajjad
Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.
This project develops a real-time air quality forecasting system using live environmental indicators and historical datasets. By integrating Metaculus predictions with local pollutant measurements (CO and NO₂), the model leverages Monte Carlo Dropout to quantify uncertainty in forecasts. A deep learning model is fine-tuned on recent data to enhance predictive accuracy, while policy recommendations are generated based on conservative thresholds to guide actionable interventions. The pipeline includes automated data ingestion, uncertainty-aware predictions, and visualization-ready outputs, providing a robust framework for environmental monitoring and decision support.
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@misc{sajjad2025ai,
title = {{AI for Environmental Decision Intelligence - The AI Forecasting Hackathon a}},
author = {Aleena Sajjad},
year = {2025},
month = nov,
note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/ai-for-environmental-decision-intelligence-the-ai-forecasting-hackathon-iptd}},
url = {https://apartresearch.com/sprints/projects/ai-for-environmental-decision-intelligence-the-ai-forecasting-hackathon-iptd}
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