Abstract
Forest fire is a disaster that causes economic and ecological damage and human life threat. Thus predicting such critical environmental issue is essential to mitigate this threat. In this paper we propose a decision tree based system for forest fire prediction. The aim being the integration of the decision tree classifier as a part of the smart sensor node architecture that allows fire prediction in automated and intelligent way without requiring human intervention. The fire prediction is based on the meteorological data corresponding to the critical weather elements that influence the forest fire occurrence, namely temperature, relative humidity and wind speed. We have obtained accuracy about 82.92% regarding the software implementation of the proposed DT based forest fire prediction system.
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Acknowledgments
This work has been done under the socio-economic project No. 14/CDTA/DGRSDT/2017.
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Abid, F., Izeboudjen, N. (2020). Predicting Forest Fire in Algeria Using Data Mining Techniques: Case Study of the Decision Tree Algorithm. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1105. Springer, Cham. https://doi.org/10.1007/978-3-030-36674-2_37
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DOI: https://doi.org/10.1007/978-3-030-36674-2_37
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