A hybrid fuzzy knowledge-based system for forest fire risk forecasting

被引：0
作者：
Neshat M. [1 ]
Tabatabi M. [2 ]
Zahmati E. [1 ]
Shirdel M. [1 ]
机构：
[1] Department of Computer Science, College of Software Engineering, Shirvan Branch, Islamic Azad University, Shirvan
[2] Remote Sensing and GIS Center, Sari University of Agricultural and Natural Resources, Sari
来源：
International Journal of Reasoning-based Intelligent Systems | 2016年 / 8卷 / 3-4期
关键词：
Fire intensity; Forest fire; Fuzzy inference system; Hybrid system; Modelling; Risk estimation;
D O I：
10.1504/IJRIS.2016.10003970
中图分类号：
学科分类号：
摘要：
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%. Copyright © 2016 Inderscience Enterprises Ltd.
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页码：132 / 154
页数：22
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