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Title: Efficient forest fire occurrence prediction for developing countries using two weather parameters
Authors: Hajj, Imad H. El
Mitri, George 
Sakr, George E
Affiliations: Institute of Environment 
Keywords: Forest fire occurrence prediction
Support Vector Machine (SVM)
Artificial Neural Network (ANN)
Feature Reduction
Weather data
Issue Date: 2011
Part of: Journal of engineering applications of artificial intelligence
Volume: 24
Issue: 5
Start page: 888
End page: 894
Forest 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.
DOI: 10.1016/j.engappai.2011.02.017
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Institute of the Environment

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