Abstract
Forest industries deserve special attention due to relations between environmental impact and social and economic development. The increase of forest fires caused by the untenable exploitation has motivated the application of concepts such as Industry/Forestry 4.0 and Internet of Forest Things (IoFT) towards improving the performance of current supply chains and assuming an environmental responsibility. This research focuses on the application of IoFT for the prediction of wildfires behavior and proposes a semantic platform for heterogeneous IoFT data aggregation that grants interoperability through semantic technologies. The dataset considered climatic- and vegetation-related data gathered by Brazilian government sensors and satellite information on fires, and Machine Learning predicted the areas affected after a fire event. Both platform and predictions were validated and Random Forest predicted the area with 89% accuracy, showing better performance than Deep Neural Network, with 79%.
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