Data Management

Predict Seagrass Habitats with Machine Learning

A new lesson from Learn ArcGIS allows you to identify locations worldwide for encouraging seagrass growth — all based on a data from your small region off Florida.

In Predict Seagrass Habitats with Machine Learning, you will assume the role of a marine ecologist who wants to find suitable habitats for seagrass growth. Because seagrasses tend to grow in similar conditions, regardless of temperatures, your Florida data sample can be applied to the entire globe because of the predictive powers of machine learning taught in this lesson.

First, you’ll create a training dataset with all the ocean variables that influence seagrass growth. You’ll put the variables into Python and use a random forest prediction model to determine which ocean area support seagrass growth. You’ll conclude by importing your predictions into ArcGIS Pro to locate areas where seagrass could grow.

About the author

John Berry

Tweeting for LearnArcGIS is ssssoooo much fun! I'm John Berry, a recovering newspaper reporter and current product engineer for Learn ArcGIS. My main task is authoring and editing lessons for Learn ArcGIS, but while nobody is looking, I also get to write blogs and tweets for the site. (And since becoming a dad in 2004, I've long mastered the fine art of dad humor, which -- properly timed -- can cause eyes to roll and laughs to start.

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