Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/396
Title: Artificial intelligence for forest fire prediction
Authors: Sakr, George E
Hajj, Imad H. El
Mitri, George 
Wejinya, Uchechukwu C.
Affiliations: Institute of Environment 
Keywords: Support Vector Machine (SVM)
Forestry
Subjects: Artificial intelligence
Ecology
Fires
Issue Date: 2011
Publisher: IEEE
Part of: 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
Conference: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (6-9 July 2010 : Montreal, QC, Canada, Canada) 
Abstract: 
Forest fire prediction constitutes a significant component of forest fire management. It plays a major role in resource allocation, mitigation and recovery efforts. This paper presents a description and analysis of forest fire prediction methods based on artificial intelligence. A novel forest fire risk prediction algorithm, based on support vector machines, is presented. The algorithm depends on previous weather conditions in order to predict the fire hazard level of a day. The implementation of the algorithm using data from Lebanon demonstrated its ability to accurately predict the hazard of fire occurrence.
URI: https://scholarhub.balamand.edu.lb/handle/uob/396
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Institute of Environment

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