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Communication dans un congrès

Comparative Study of Different Data Mining Techniques in Predicting Forest Fire in Lebanon and Mediterranean

Abstract : Forest fire is one of the most complex phenomena which can cause great economic losses and make eco-environment seriously disordered. Forest fire has caused the loss of many green acres in Lebanon due to the lack of governmental policies in order to mange forest fires. This paper presents an overview of the exciting applications of data mining techniques in different fields. This study aims to predict forest fires in North Lebanon in order to reduce fire occurrence based on 4 meteorological parameters (Temperature, Humidity, Precipitation and Wind speed) using different data mining techniques: Neural networks, decision tree (J48), fuzzy logic, support vector machine (SVM) and linear discriminant analysis (LDA). A comparative study is then made to find the best performing technique tending to manage such a natural crisis. Decision tree (J48) recorded the best accuracy in forest fire prediction (97.8%).
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Communication dans un congrès
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https://hal.univ-angers.fr/hal-02517393
Contributeur : Marie-Françoise Gerard <>
Soumis le : mardi 24 mars 2020 - 15:18:33
Dernière modification le : mardi 28 avril 2020 - 17:17:20

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Nizar Hamadeh, Ali Karouni, Bassam Daya, Pierre Chauvet. Comparative Study of Different Data Mining Techniques in Predicting Forest Fire in Lebanon and Mediterranean. SAI Intelligent Systems Conference 2016 (IntelliSys 2016), Sep 2016, Londres, United Kingdom. pp.747-762, ⟨10.1007/978-3-319-56994-9_51⟩. ⟨hal-02517393⟩

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