Machine Learning for Sustainability

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.

Global Irrigation Extent, as per our ML Model

Choose your destination:

“I want to look at your results”:

We have the following interactive maps:

“I want to use your results”:

Maps are available:

“I want a quick summary of what you did”:

Watch our lightning talk (5 min) at Google Geo for Good summit. Alternatively, you can read this blog post.

“I have the time, tell me more”:

You can read our published journal article (alternative link). You can also look at our model assessment map.

“I have a lot of time, I want to do this myself”:

Source code and system design is on my Github. If you like to build something similar, you can read about how we built our model.

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).

SAR picture for a part of Central Valley, California. SAR values standardized on non-irrigated class distribution.

See this post for more detail. Source notebook is available as well.

Quantifying Large Hailstorms Across the US

Large hailstorms in the US: likelihood based on historical occurrence (1995-2020)

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.