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|Title:||Artificial intelligence for forest fire prediction||Authors:||Sakr, George E
Hajj, Imad H. El
Wejinya, Uchechukwu C.
|Affiliations:||Institute of Environment||Keywords:||Support Vector Machine (SVM)
|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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