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FireWatch displaying predicted wildfire severity in California.
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FireWatch displaying predicted wildfire severity throughout the U.S.
Inspiration
We were moved by the amount of destruction caused by wildfires in America, most prominently on the west coast in recent years.
What it does
We use several attributes, including meteorological data (provided by an API) to create a novel model which predicts the likeliness and severity of wildfires.
How we built it
The model was built using Python primarily, with R and Stata supplementing when necessary. Within Python, we primarily used pandas, numpy, and scikit-learn. The front end was done in JavaScript, HTML, CSS, and Folium.
Our particular model used a Random-Forest ensemble algorithm. We also trained a SVM model, which was outperformed by the former.
Challenges we ran into
The machine-learning algorithms took extensive time and memory to train. Some trial-and-error was required before we decided on our final model.
Accomplishments that we're proud of
We did it!!!
What we learned
How to use scikit-learn and caret.
What's next for FireWatch
Our model would benefit from more recent data, given the rapidly changing climate conditions.
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