Here is some work I’ve one on using machine learning for sustainability. My main focus has been sustainable use of water.
Global Water Use for Irrigation
Globally, agriculture consumes 65-70% of freshwater. In this work, I predict irrigation globally for the years 2001-2015 and show how it has changed in this timeframe.
Our project uses satellite and climate data to fit a random forest model and classify the world into “high”, “low-to-medium” and “no” irrigation areas. Published in Advances in Water Resources, 2021.
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Detecting Irrigation with Radar Satellites
With a little bit of preprocessing, SAR data and image-processing neural networks can detect irrigation in California with an accuracy of 95% (on a balanced dataset).
Quantifying Large Hailstorms Across the US
Large hailstorms can damage solar panels, so it’s useful to quantify historical large hailstorm occurrences across the US to plan new solar panel installations.