A computer-system that classifies the prefectures of Greece in forest fire risk zones using fuzzy sets

Abstract

All of the Mediterranean countries face a serious forest fire problem. The main factors that affect the problem of forest fires in Greece are vegetation, climate conditions and most of all, arson (Proceedings of Forest Fires in Greece, Thessaloniki, 1990, p. 97). In Greece, after 1974, the number of forest fires and the total burned areas have risen dramatically. The design of an effective fight and prevention policy is a very important matter, as it can minimize the destruction. This paper describes an expert system that classifies the prefectures of Greece into forest fire risk zones, using a completely new methodology. The concept of fuzzy expected intervals (F.E.I.) was defined by Kandel and Byatt (Proc. IEEE, 66, 1978, 1619) and offered a very good approach towards forest fire risk classification. Fuzzy expected intervals are narrow intervals of values that best describe the forest fire problem in the country or a part of the country for a certain time period. Fuzzy logic was applied to produce a F.E.I. for each prefecture of the country. A successful classification of the prefectures of Greece (in forest fire risk zones) was performed by the expert system by comparing the produced fuzzy expected intervals to each other and by using a supervised machine learning algorithm that assigns a certain weight of forest fire risk to each prefecture (Machine Learning, John Wiley and Sons, 1995).