Transcription of A Data Mining Approach to Predict Forest Fires using ...
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A Data Mining Approach to Predict Forest Fires using ...
A Data Mining Approach to Predict Forest Firesusing Meteorological DataPaulo Cortez1and An bal Morais1 Department of Information Systems/R&D Algoritmi Centre, University of Minho,4800-058 Guimar aes, home page: Fires are a major environmental issue, creating economical andecological damage while endangering human lives. Fast detection is a key ele-ment for controlling such phenomenon. To achieve this, one alternative is to useautomatic tools based on local sensors, such as provided by meteorological sta-tions. In effect, meteorological conditions ( temperature, wind) are known toinfluence Forest Fires and several fire indexes, such as the Forest Fire Weather In-dex (FWI), use such data. In this work, we explore a Data Mining (DM) approachto Predict the burned area of Forest Fires . Five different DM techniques, Sup-port Vector Machines (SVM) and Random Forests, and four distinct feature se-lection setups ( using spatial , temporal, FWI components and weather attributes),were tested on recent real-world data collected from the northeast region of Por-tugal.
the spatial and temporal attributes. Only two geographic features were included, the X and Y axis values where the fire occurred, since the type of vegetat ion presented a low quality (i.e. more than 80% of the values were missing). After consulting the Mon-tesinho fire inspector, we selected the month and day of the week temporal variables.
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