Predicting Burned Areas of Forest Fires: an Artificial Intelligence Approach

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@Article{Castelli:2015:FireEcology,

• author = "Mauro Castelli and Leonardo Vanneschi and Ales Popovic",
• title = "Predicting Burned Areas of Forest Fires: an Artificial Intelligence Approach",
• journal = "Fire Ecology",
• year = "2015",
• volume = "11",
• number = "1",
• pages = "106--118",
• keywords = "genetic algorithms, genetic programming, geometric semantic genetic programming",
• ISSN = "1933-9747",
• publisher = "The Association for Fire Ecology",
• DOI = "doi:10.4996/fireecology.1101106",
• size = "13 pages",
• abstract = "Forest fires importantly influence our environment and lives. The ability of accurately predicting the area that may be involved in a forest fire event may help in optimizing fire management efforts. Given the complexity of the task, powerful computational tools are needed for predicting the amount of area that will be burned during a forest fire. The purpose of this study was to develop an intelligent system based on genetic programming for the prediction of burned areas, using only data related to the forest under analysis and meteorological data. We used geometric semantic genetic programming based on recently defined geometric semantic genetic operators for genetic programming. Experimental results, achieved using a database of 517 forest fire events between 2000 and 2003, showed the appropriateness of the proposed system for the prediction of the burned areas. In particular, results obtained with geometric semantic genetic programming were significantly better than those produced by standard genetic programming and other state of the art machine learning methods on both training and out-of-sample data. This study suggests that deeper investigation of genetic programming in the field of forest fires prediction may be productive.",
• notes = "The Journal of the Association for Fire Ecology",

}

Genetic Programming entries for Mauro Castelli Leonardo Vanneschi Ales Popovic

Citations