• Recent Forest Fires have caused a lot of nuisance
  • Forest Fires in general cause huge economic and personal loss to a lot of people, so reducing them could help a lot.

What it does

It predicts the area burnt at a location selected on a map by a forest fire.

How I built it

  • When the user clicks the button on the web page, the Heroku API is sent the latitude and longitude of the place selected on the map.
  • The python script then fetches weather data from the openweathermap API.
  • This data is used to calculate the various Fire Weather Indices.
  • The FWIs and the raw data from the API are fed into the Deep Regression model which outputs ## Challenges I ran into
  • I rarely work with front end, so getting all of the css and html to work was challenging.
  • Setting up the API was challenging as I had to go through a lot of documentation to figure out how to do it.
  • Getting the formulas for calculating the Fire Weather Indices was extremely difficult as there is zero to no documentation on how to do it. I ended up looking at the source js code for some websites to find a link that provided me with what I needed.

Accomplishments that I'm proud of

  • Integrating everything seamlessly, so that I have several parts hosted at different locations like Heroku and Github.
  • Getting my machine learning model to have a low error on the test data.

What I learned

  • A lot about js, as that was what I used to communicate between the API and the website
  • How to make websites.
  • Different tricks when approaching a machine learning problem.

What's next for Forest Fire Predictor

  • Make it work throughout the World, also improve the variance of my model.
  • Some more training data could help.

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