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Using correlative data analysis to develop weather index that estimates the risk of forest fires in Lebanon & Mediterranean: Assessment versus prevalent meteorological indices

Abstract : Forest fires are among the most dangerous natural threats that bring calamities to a community and can turn it totally upside down. In this paper, to enable a prevention mechanism, we rely on analytics to build a novel fire danger index model that predicts the risk of a developing fire in north Lebanon. We use correlation methods such as statistical regression, Pearson, Spearman and Kendall’s Tau correlation to identify the most affecting parameters on fire ignition during the last six years in north Lebanon. The correlations of these attributes with fire occurrence are studied in order to develop the fire danger index. The strongly correlated attributes are then derived. We rely on linear regression to model the fire index as function of a reduced set of weather parameters that are easy to measure. This is critical as it facilitates the application of such prevention models in developing countries like Lebanon. The outcomes resulting from validation tests of the proposed index show high performance in the Lebanese regions. An assessment versus common widespread weather models is then made and has showed the significance the selected parameters. It is strongly believed that this index will help improve the ability of fire prevention measures in the Mediterranean basin area.
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https://hal.univ-angers.fr/hal-02527872
Contributeur : Okina Université d'Angers <>
Soumis le : mercredi 1 avril 2020 - 15:10:41
Dernière modification le : mercredi 1 avril 2020 - 20:12:41

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Nizar Hamadeh, Ali Karouni, Bassam Daya, Pierre Chauvet. Using correlative data analysis to develop weather index that estimates the risk of forest fires in Lebanon & Mediterranean: Assessment versus prevalent meteorological indices. Case Studies in Fire Safety, 2017, 7, pp.8-22. ⟨10.1016/j.csfs.2016.12.001⟩. ⟨hal-02527872⟩

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