Fundamental fuzzy relation concepts of a D.S.S. for the estimation of natural disasters' risk (The case of a trapezoidal membership function)

• Published in: Mathematical and computer modelling Vol. 42; no. 7; pp. 747 - 758
• Main Authors: Iliadis, L.S., Spartalis, S.I.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2005; Elsevier Science
• Subjects: Exact sciences and technology; Fuzzy sets; Inference from stochastic processes; time series analysis; Mathematical logic, foundations, set theory; Mathematics; Membership functions; Methods of scientific computing (including symbolic computation, algebraic computation); Natural disaster risk; Numerical analysis. Scientific computation; Probability and statistics; Sciences and techniques of general use; Set theory; Soft-computing; Statistics; Soft-computing; Fuzzy sets; Membership functions; Natural disaster risk; Membership function; Decision support system; Statistical estimation; Computing; Fuzzy set; Prevention; Statistical test; Forecasting theory; Inference mechanisms; Applied mathematics; Fuzzy relation; Trapezoidal method; Forest fire risk; Mathematical model
• ISSN: 0895-7177; 1872-9479
• DOI: 10.1016/j.mcm.2005.09.004

The effective protection from natural disasters requires the development of a rational and sensible protection and prevention policy. This project deals with the development and testing of a decision support system that acts on two levels. On the first level, it estimates the annual forest fire risk for each area of Greece using a fuzzy Trapezoidal membership function. Reference is done to past work in this area and past results of forest fire risk estimation (using other models) were compared to the results of this system. On the second level, it forecasts a narrow expected closed interval for the burned area, using a fuzzy expected interval model. It is the first time that such forecasts are produced and such results are obtained. Physically and operationally, the decision support system consists of two parts. The risk estimation part is more straightforward and it was developed in MS-Access in order to have the ability to store and use a vast amount of data. The forecasting part was developed in a decision support system shell, in order to have a sound Inference mechanism.