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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%. Refereed/Peer-reviewed

Nowadays, expert systems play a major role in better doing of complex tasks and giving advice to the experts because expertism is a specialised knowledge. Overall, expert systems are used to solve the problems for which there is not an accurate knowledge and a particular algorithm. Understanding the atmospheric phenomena and their role in human life are the most important and affecting issues in human societies. In meteorology, it is important to identify the type of clouds. By monitoring from the Earth's surface (seeing bottom view of the cloud) and using satellites (seeing top view of the cloud), we can identify the variety of clouds. A fuzzy inference system with the specialists' knowledge of meteorology is designed in this paper and its aims are detection of the cloud type through extracting knowledge from satellite images of the cloud upper portions. The used data are extracted from the reputable website of UCI called cloud dataset. This dataset is gathered by Philip Collard in two ranges of IR and VISIBLE. Using the experts' knowledge, this system determines the type of cloud with an accuracy level of 88.25% ± 0.5 and according to experts' opinion; the results are suitable and acceptable Refereed/Peer-reviewed