Introduction to non-linear optimization
Introduction to non-linear optimization
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Forest fire risk zone mapping from satellite imagery and GIS
International Journal of Applied Earth Observation and Geoinformation
Development and application of a system for dynamic wildfire risk assessment in Italy
Environmental Modelling & Software
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Engineering Applications of Artificial Intelligence
Statistics based predictive geo-spatial data mining: forest fire hazardous area mapping application
APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications
Support Vector Machines to Define and Detect Agitation Transition
IEEE Transactions on Affective Computing
Engineering Applications of Artificial Intelligence
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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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.