A hybrid fuzzy knowledge-based system for forest fire risk forecasting
by Mehdi Neshat; Masoud Tabatabi; Ebrahim Zahmati; Mohhammad Shirdel
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 8, No. 3/4, 2016

Abstract: Fire is one of the most important factors destroying forest ecosystems which can result in negative economic and social consequences. Quick detection can be an effective factor in controlling this destructive phenomenon. This research was aimed at designing a hybrid fuzzy expert system in order to predict the size of forest fires effectively and accurately. The data were taken from the authentic dataset named forest fire in University of California (UCI). In fact, the proposed system is a hybrid of six fuzzy inference systems with acceptable performances according to their results. The accuracy of predicting the size of fire was 81.2%.

Online publication date: Fri, 17-Mar-2017

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