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| Neural Network Approach to Predict Forest Fires using Meteorological Data |
Mutasim Mahmoud Al-Kahlout, Ahmed Mahmoud Abu Ghaly, Donia Zaher Mudawah, Samy S. Abu-Naser
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Abstract:
Forest fires are a major environmental issue, creating economical and ecological damage while endangering human lives. Fast detection is a key element for controlling such phenomenon. To achieve this, one alternative is to use automatic tools based on local sensors, such as provided by meteorological stations. In effect, meteorological conditions (e.g. temperature, wind) are known to influence forest fires and several fire indexes, such as the forest Fire Weather Index (FWI), use such data. In this work, we explore a Just Neural Network (JNN) approach to predict the burned area of forest fires were tested on recent real-world data collected from the northeast region of Portugal. The best accuracy we achieved was 98.75 percent. Such knowledge is particularly useful for improving firefighting resource management (e.g. prioritizing targets for air tankers and ground crews).
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