DocumentCode :
2248097
Title :
Artificial intelligence for forest fire prediction
Author :
Sakr, George E. ; Elhajj, Imad H. ; Mitri, George ; Wejinya, Uchechukwu C.
Author_Institution :
American Univ. of Beirut, Beirut, Lebanon
fYear :
2010
fDate :
6-9 July 2010
Firstpage :
1311
Lastpage :
1316
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.
Keywords :
artificial intelligence; ecology; fires; forestry; support vector machines; artificial intelligence; fire hazard level; forest fire management; forest fire risk prediction algorithm; support vector machine; weather condition; Data mining; Equations; Fires; Prediction algorithms; Support vector machines; Weather forecasting; Forest Fire Prediction; Machine Learning; SVM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Intelligent Mechatronics (AIM), 2010 IEEE/ASME International Conference on
Conference_Location :
Montreal, ON
Print_ISBN :
978-1-4244-8031-9
Type :
conf
DOI :
10.1109/AIM.2010.5695809
Filename :
5695809
Link To Document :
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