Predictive Analytics & Imagery for Environmental Monitoring
Shambhavi Adhikari, Yeji Kim, Dilrose Karakattil · Team EmissioNerds
Submitted to AI Policy Hackathon at Johns Hopkins University. Sprint projects are early-stage work by participants, not Apart Research publications.
Climate change poses multifaceted challenges, impacting health, food security, biodiversity, and the economy. This study explores predictive analytics and satellite imagery to address climate change effects, focusing on deforestation monitoring, carbon emission analysis, and flood prediction. Using machine learning models, including a Random Forest for emissions and a Custom U-Net for deforestation, we developed predictive tools that provide actionable insights. The findings show high accuracy in predicting carbon emissions and flood risks and successful monitoring of deforestation areas, highlighting the potential for advanced monitoring systems to mitigate environmental threats.
Reviews
This project is well-motivated and a great opportunity to apply machine learning. However, the specific subtask the authors chose feels somewhat loosely connected to the central theme. Strengthening the project could involve a more detailed discussion on the connections between the identified problems, along with a unified perspective on the methodologies applied to each subproblem. This approach would bring greater cohesion and clarity to the project.
Relevant problem with an impressive implementation in such a short time-span. To develp this further, I would like to see how your method compares to current SOTA models for such predictions. To make this work more impactfull you could include a discussion on how these results could guide policy.
This project tries to touch on the true need for technical innovative measures to detect contributing factors to climate change. It uses tried and true machine learning methods that are interpritable and well studied.
Would benefit from more integration & synchronization between the three different components (emission, flood, deforestation)
Cite this project
@misc{adhikari2024predictive,
title = {{Predictive Analytics \& Imagery for Environmental Monitoring}},
author = {Shambhavi Adhikari and Yeji Kim and Dilrose Karakattil},
year = {2024},
month = oct,
note = {Submitted to AI Policy Hackathon at Johns Hopkins University, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/predictive-analytics-imagery-for-environmental-monitoring}},
url = {https://apartresearch.com/sprints/projects/predictive-analytics-imagery-for-environmental-monitoring}
}More from AI Policy Hackathon at Johns Hopkins University
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