Comparative Study of Different Data Mining Techniques in Predicting Forest Fire in Lebanon and Mediterranean
| Autor: | Ali Karouni, Bassam Daya, Nizar Hamadeh, Pierre Chauvet |
|---|---|
| Přispěvatelé: | Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers (UA), Université Libanaise |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
040101 forestry
Mediterranean climate 021110 strategic defence & security studies Artificial neural network Computer science 0211 other engineering and technologies Decision tree 04 agricultural and veterinary sciences 02 engineering and technology 15. Life on land Linear discriminant analysis computer.software_genre Fuzzy logic Wind speed Support vector machine Data mining applications Forest fire prediction Meteorological data [SPI]Engineering Sciences [physics] C4.5 algorithm 0401 agriculture forestry and fisheries Data mining computer |
| Zdroj: | SAI Intelligent Systems Conference 2016 (IntelliSys 2016) SAI Intelligent Systems Conference 2016 (IntelliSys 2016), Sep 2016, Londres, United Kingdom. pp.747-762, ⟨10.1007/978-3-319-56994-9_51⟩ Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016, pp.747-762, 2018, ⟨10.1007/978-3-319-56994-9_51⟩ Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 ISBN: 9783319569932 |
| DOI: | 10.1007/978-3-319-56994-9_51⟩ |
| Popis: | International audience; 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%). |
| Databáze: | OpenAIRE |
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