MOVING TOWARD LESS UNCERTAINTY SEISMIC RISK PREDICTION USING GRANULAR COMPUTING ALGORITHM

سال انتشار: 1394
محل انتشار: هفتمین کنفرانس بین المللی زلزله شناسی و مهندسی زلزله
کد COI اختصاصی: SEE07_411
زبان مقاله: انگلیسی
تعداد مشاهده: 252

نویسندگان

M.Sc. Graduate, GIS Division, Department of Surveying and Geomatics Engineering, College of Eng., University of Tehran, Tehran, Iran

M. R. DELAVAR

Center of Excellence in Geomatics Eng. and Disaster Management, Dept. of Surveying and Geomatics Eng., College of Eng., University of Tehran, Tehran, Iran

M. ZARE

Seismology Research Center, International Institute of Earthquake Engineering and Seismology (IIEES), Tehran, Iran

A. MOHSENI

Research Associate, Seismology Research Center, International Institute of EarthquakeEngineering and Seismology (IIEES), Tehran, Iran

چکیده

Iran is one of the seismically active areas of the world due to its position in the Alpine-Himalayan mountain system. So, strong earthquakes in this area have caused a high toll of casualties and extensive damage over the last centuries.Pre-determining locations and intensityof seismic area of a city is considered as a complicateddisaster management problem. As, this problem generally depends on various criteria, one of the most important challenges concerned is the existence of uncertainty regarding inconsistency in combining influencing criteria and extracting more consistent knowledge forthe next predictions. To overcome this problem, this paper proposes a new approach for seismic risk knowledge discovery based on granular computing theory. One of the significant properties of this method is inductionof more compatible rules having zero inconsistency fromexisting databases. Furthermore, in this approach non redundant covering rules will be extracted for consistent classification where one object maybe classified with two or more non-redundant rules.In this paper, the seismic risk of the area between 58˚ 24' E, 60˚ 24' E Longitude and 27˚ 45' N, 29˚ 25' N Latitude around occurred near Reygan (Kerman Province), South-East of Iran where a devastating earthquake happened is considered as the study area. The result of this paper exhibits why granular computing is proposed to decrease the uncertainty of knowledge extracted from input large dataset.

کلیدواژه ها

Data Mining, Uncertainty, Earthquake

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