Tehran’s seismic vulnerability classification using granular computing approach

Hadis Samadi Alinia1, M. R. Delavar2
1GIS Division, Department of Surveying and Geomatics Engineering, College of Engineering, University of Tehran, Tehran, Iran
2Center of Excellence in Geomatics Engineering and Disaster Management, Department of Surveying and Geomatics Engineering, College of Engineering, University of Tehran, Tehran, Iran

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Aghataher R, Delavar MR, Kamalian N (2005) Weighing of contributing factors in vulnerability of cities against earthquakes. Proc. map Asia conference, Jakarta, Indonesia, pp 22–25

Amiri AR, Delavar MR, Zahrai SM and Malek MR (2007) Tehran seismic vulnerability assessment using Dempster–Shafer theory of evidence. Proc. map Asia conference, Kuala Lumpur, Malaysia, August 14–16, p 9

Berberian M (1976) Contribution to the seismotectonics of Iran (part II). Geological Survey of Iran, Report no. 39

Berberian M, Yeats RS (1999) Patterns of historical earthquake rupture in the Iranian plateau. Bull Seismol Soc Am 89(1):120–139

Berberian M, Yeats RS (2001) Contribution of archaeological data to studies of earthquake history in the Iranian plateau. J Struct Geol 23:563–584

Demri S, Orlowska E (1998) Logical analysis of indiscernibility. In: Orlowska E (ed) Incomplete information: rough set analysis. Physica-Verlag, Heidelberg, pp 347–380

Ganascia J-G (1993) TDIS: an algebraic formalization. Proc IJCAI 1993:1008–1015

Jafari MK, Kamalian M, Razmkhah A, Sohrabi A (2004) North of Tehran site effect microzonation. 13th World conference on earthquake engineering, Vancouver, BC, Canada

Jahankhah M, Moshiri B, Delavar MR, Zare M (2009) The evidential reasoning approach for a multi attribute decision making method in geospatial information. In: Journal of Control. ISICE, a Joint Publication of the Iranian Society of Instrument and Control Engineers and the K.N. Toosi University of Technology, Vol. 3, No. 3, pp 52–63

JICA (2000) The study on seismic microzoning of the greater Tehran area in the Islamic Republic of Iran, Final report. Japan International Cooperation Agency (JICA)

JICA (2001) Frontier Magazine

Lin TY, Yao YY, Zadeh LA (eds) (2002) Data mining, rough sets and granular computing. Physica-Verlag, Heidelberg

Moore RE (1966) Interval analysis. Prentice-Hall, Englewood Cliffs, NJ

Nazari H, Ritz J-F, Salamati R, Solaymani S, Balescu S, Michelot J-L Ghassemi A, Talebian M, Lamothe M and Massault M (2007) Paleoseismological analysis in Central Alborz, Iran. 50th Anniversary earthquake conference commemorating the 1957 Gobi-Altay earthquake

Pawlak Z (1982) Rough sets. Int J Comp Inf Sci 11:341–356

Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht

Quinlan JR (1983) Learning efficient classification procedures and their applications to chess end-games. In: Machine learning: an artificial intelligence approach, Vol. 1

Ritz J-F, Balescu S, Soleymani S, Abbassi M, Nazari H, Feghhi K, Shabanian E, Tabassi H, Farbod Y, Lamothe M, Michelot J-L, Massault M, Chéry J, Vernant P (2003) Determining the long-term slip rate along the Mosha Fault, Central Alborz, Iran. Fourth international conference of earthquake engineering and seismology

Samadi Alinia H (2010) Assessment of vulnerability of earthquake in Tehran using granular computing model, M.Sc. thesis. (in Persian with English abstract). College of Engineering, University of Tehran

Samadi Alinia H, Delavar MR, Yao YY (2010a) An ID3-improved approach of for optimum rule mining through granular computing search algorithm. Proc. spatial accuracy conference, 20–23 July, Leicester, UK

Samadi Alinia H, Delavar MR, Yao YY (2010b) Support and confidence parameters to induct decision rules to classify Tehran’s seismic vulnerability. Proc. 6th international symposium on geo-information for disaster management (Gi4DM), September 15, Torino, Italy

Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton, NJ

Shafiee A, Kamalian M, Jafari MK, Hamzehloo H (2011) Ground motion studies for microzonation in Iran. In: Natural hazards, Vol. I. Int J Sci Bus

Silavi T, Delavar MR, Malek MR, Kamalian N, Karimizand K (2006) An integrated strategy for GIS-based fuzzy improved earthquake vulnerability assessment. The second international symposium on geo-information for disaster management. ISPRS, India

Wille R (1992) Concept lattices and conceptual knowledge systems. Comp Math Appl 23:493–515

Yao YY (2000) Granular computing: basic issues and possible solutions. In: Wang PP (ed) Proceedings of the 5th joint conference on information sciences, Atlantic City, NJ, USA. Association for intelligent machinery, Vol. I, pp 186–189

Yao YY (2001) On modelling data mining with granular computing. Proceedings 25th annual international computer software and applications conference (COMPSAC’01), pp 638–643

Yao YY (2004) A partition model of granular computing. LNCS Trans Rough Sets I:232–253

Yao JT, Yao YY (2002a) Induction of classification rules by granular computing. Proceedings, the third international conference on rough sets and current trends in computing. Lecture notes in artificial intelligence, pp 331–338

Yao JT, Yao YY (2002b) A granular computing approach to machine learning, Proceedings of the first international conference on fuzzy systems and knowledge discovery (FSKD’02), Singapore, pp 732–736

Yao YY, Zhong N (1999) Potential applications of granular computing in knowledge discovery and data mining. In: Proceedings of world multiconference on systemics, cybernetics and informatics, Orlando, FL, July 14—18, pp 573–580

Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Set Syst 90:111–127

Zadeh LA (1998) Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Computing 2(1):23–25

Zare M (2003) Seismic hazard analysis of Tehran, ongoing research project. IIEES, Tehran, Iran

Zare M, Bard PY, Ghafory-Ashtiany M (1999) Site categorization for the Iranian strong motion network. Soil Dyn Earthq Eng 18:101–123

Zhao Y, Yao YY (2005) Interactive user-driven classification using a granule network, Proceedings of the fifth international conference of cognitive informatics, pp 250–259

Zhao Y, Yao YY, Yan M (2007) ICS: an interactive classification system. Proceedings of the 20th Canadian conference on artificial intelligence (CAI’07), pp 134–145

Zhong W, He J, Harrison R, Tai PC, Pan Y (2007) Clustering support vector machines for protein local structure prediction. Exp Syst Appl 32(2):518–526