Original article was published by Martin Tin on Artificial Intelligence on Medium
Forest fires are a huge problem. In the US alone, forest fires have burned down 6.7 million acres of land. That’s about 0.4% of US land! These fires have destroyed countless habitats and buildings and have caused noticeable amounts of carbon emissions. So how do we prevent forest fires?
It turns out that we fight fire with fire!
One popular approach to forest fire prevention is called controlled burning. This is a method used to burn a section of forest in a controlled and purposeful manner. In controlled burning, firefighters light up an area of forestry and use fire breaks (gaps between vegetation and other combustible materials) to allow firefighters to control the fire. Controlled burning nullifies or dulls the spread and impact of a forest fire by removing kindling which contribute to the spread of forest fires. (dead grass, fallen tree branches, thick undergrowth)
However, despite its usefulness, controlled burning has many disadvantages. For one, it can become uncontrollable and often burns excessive amounts of trees. Controlled burning is also expensive and can devastate ecosystems.
For all these reasons, we want to minimize the amount of controlled burning. We can do so by using artificial intelligence. (ai)
Using a supervised regression model, we can map the connections between these variables and make estimates on the spread of a fire given it occurred in a specific area.
With a model like this, it is possible to prevent future forest fires and also minimize the detrimental effects of controlled burning!
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