That's Fire

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  Machine Learning generated numbers telling the user different probabilities and information about the county

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  Map UI of California

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  Zoomed version of map, as the differences in opacity show the risk of a wildfire of all the counties in California

Inspiration

With the California wildfires being a prominent problem in the US, we were inspired to solve this problem by creating an app that predicts the risk of wildfires, as homeowners and residents can make quick decisions regarding their safety and to conduct prevention techniques.

What it does

Our app shows the high risk areas in California for wildfires, including the chances of smoke appearing in different counties. Along with this, it shows weather information about the counties, including humidity, temperature, wind, precipitation, and more.

How we built it

First we found environmental datasets to train machine learning models to predict wildfires and smoke. Then, we used flask to obtain information from the user and to create the backend of the app. We found apis to obtain data for all the counties in California to input into the models to obtain the probabilities of wildfires and smoke.

Challenges we ran into

Some challenges included finding proper datasets, integrating flask with our machine learning models, integrating the backend with the front end.

Accomplishments that we're proud of

Machine learning, flask, apis, front end

What we learned

What's next for That's Fire

Built With

• flask
• flutter
• python