Mike Mahoney
PhD Student at SUNY-ESF and ESIP Community Fellow
Focusing on visualization, VR and ML as ways to think about large-scale systems
These slides: mm218.dev/esip2022
85% reduction in GHG emissions by 2050
Net zero emissions statewide by 2050
Lands & forests make up the difference
Need a way to monitor sequestration
Figure from Shirer, R. and Zimmerman, C. (2010) Forest Regeneration in New York State. The Nature Conservancy.
Linear regression combining predictions from:
Random forest
Gradient boosting machine
Support vector machine
https://blogs.scientificamerican.com/observations/the-medias-coverage-of-ai-is-bogus/
The press will have you believe that machine learning can reliably predict whether you’re gay, whether you’ll develop psychosis, whether you’ll have a heart attack and whether you’re a criminal—as well as other ambitious predictions such as when you’ll die and whether your unpublished book will be a bestseller. It’s all a lie.
Here’s how the lie works. Researchers report high “accuracy,” but then later reveal—buried within the details of a technical paper—that they were actually misusing the word “accuracy” to mean another measure of performance related to accuracy but in actuality not nearly as impressive.
https://www.esipfed.org/merge/student-fellow-blog/trust-in-machine-learning-guest-blog
Figure from https://github.com/leekgroup/postpi/.
Scale | RMSE | MBE | R2 |
---|---|---|---|
Plot-pixel comparison | 39.60 | 1.88 | 0.76 |
8,660 ha hex | 33.27 | 3.56 | 0.76 |
78,100 ha hex | 23.38 | 1.64 | 0.80 |
216,500 ha hex | 21.17 | 0.75 | 0.81 |
This work was financially supported by the Climate & Applied Forest Research Institute at SUNY-ESF.
Slides available at mm218.dev/esip2022