Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/1935
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dc.contributor.authorHajj, Imad H. Elen_US
dc.contributor.authorMitri, Georgeen_US
dc.contributor.authorSakr, George Een_US
dc.date.accessioned2020-12-23T09:03:13Z-
dc.date.available2020-12-23T09:03:13Z-
dc.date.issued2011-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/1935-
dc.description.abstractForest fire occurrence prediction plays a major role in resource allocation, mitigation and recovery efforts. This paper compares two artificial intelligence based methods, artificial neural networks (ANN) and support vector machines (SVM), utilizing a reduced set of weather parameters. Using a reduced set of parameters results in an efficient and reduced cost prediction system especially for developing countries. In this paper the aim is to predict forest fire occurrence by reducing the number of monitored features, and eliminating the need for weather prediction mechanisms. The reason is to reduce errors due to inaccuracies in weather prediction. The challenge is to choose a limited number of easily measurable features in the aim of reducing the cost of the system and its maintenance. At the same time, the chosen features must have a high correlation with the risk of fire occurrence. A literature review of forest fire prediction methods divided into systems/indices, and artificial intelligence is provided. The two fire danger prediction algorithms utilize relative humidity and cumulative precipitation to output a risk estimate. The assessment of these algorithms, using data from Lebanon, demonstrated their ability to accurately predict the risk of fire occurrence on a scale of four levels.en_US
dc.format.extent6 p.en_US
dc.language.isoengen_US
dc.subjectForest fire occurrence predictionen_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectFeature Reductionen_US
dc.subjectWeather dataen_US
dc.titleEfficient forest fire occurrence prediction for developing countries using two weather parametersen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1016/j.engappai.2011.02.017-
dc.contributor.affiliationInstitute of Environmenten_US
dc.description.volume24en_US
dc.description.issue5en_US
dc.description.startpage888en_US
dc.description.endpage894en_US
dc.date.catalogued2018-02-02-
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=https://doi.org/10.1016/j.engappai.2011.02.017en_US
dc.identifier.OlibID177423-
dc.relation.ispartoftextJournal of engineering applications of artificial intelligenceen_US
dc.provenance.recordsourceOliben_US
Appears in Collections:Institute of the Environment
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