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Earthquake Events Modeling Using Multi-criteria Decision Analysis in Iran

  • Marzieh MokarramEmail author
  • Hamid Reza Pourghasemi
Chapter
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 48)

Abstract

Kerman Province in Iran is known as an earthquake prone area, with different serious damages. In this study, GIS-based ordered weight averaging (OWA) with fuzzy quantifier algorithm is used to model earthquake events in north of Kerman Province, Iran. For this aim, at first using attraction model was tried to increase DEM resolution from 30 to 10 m. Then, using the mentioned DEM, three layers such as aspect, slope, and elevation was prepared. Also, different layers including lithology, land use, river, road, fault, and earthquake occurrences were prepared in ArcGIS software. Subsequently, the importance of each factor in earthquake events was defined using trapezoidal membership function. Finally, the earthquake events map with different risk level (six levels) was prepared using OWA method. The results showed that with decreasing risk (no trade-off), many parts of the study area had not earthquake events hazard. While, with increasing risk (no trade-off), all of the study area had earthquake events hazard. Low level of risk and no trade-off had the highest area in the very low class (98%), while high level of risk and average trade-off had the highest area in the very low class (15.62%). So, for the study where has high earthquake should use low level risk maps in order to better management and damage decreasing.

Keywords

Earthquake events modeling Ordered weighted averaging (OWA) Fuzzy quantifiers GIS Iran 

Notes

Acknowledgements

The authors would like to thanks to all personnel of Agricultural Jihad of Fars province for their kind help.

References

  1. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81CrossRefGoogle Scholar
  2. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31.  https://doi.org/10.1016/J.GEOMORPH.2004.06.010CrossRefGoogle Scholar
  3. Balaji D, Sankar R, Karthi S (2002) GIS Approach for disaster management through awareness-an overview. Paper presented at the proceedings of the 5th annual international conference-map India, New Delhi, 6–8 Feb 2002Google Scholar
  4. Champatiray PK, Perumal RJ, Thakur VC, Bhat MI, Malik MA, Singh VK et al (2005) A quick appraisal of ground deformation in Indian region due to the October 8, 2005 earthquake, Muzaffarabad, Pakistan. J Indian Soc Remote Sens 33(4):465–473CrossRefGoogle Scholar
  5. Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65(3):2105–2128CrossRefGoogle Scholar
  6. Gorsevski PV, Jankowski P (2008) Discerning earthquake events using rough sets. Comput Environ Urban Syst 32:53–65CrossRefGoogle Scholar
  7. Gorsevski PV, Jankowski P (2010) An optimized solution of multi-criteria evaluation analysis of earthquake events using fuzzy sets and Kalman filter. Comput Geosci 36:1005–1020CrossRefGoogle Scholar
  8. Gorsevski PV, Jankowski P, Gessler PE (2006) An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control Cybern 35:21–141Google Scholar
  9. Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272–299CrossRefGoogle Scholar
  10. Hadji R, Chouabi A, Gadri L, Raïs K, Hamed Y, Boumazbeur A (2016) Application of linear indexing model and GIS techniques for the slope movement susceptibility modeling in Bousselam upstream basin. Northeast Algeria Arab J Geosci 9(3):1–18Google Scholar
  11. Henning BD (2011) Gridded cartograms as a method for visualising earthquake risk at the global scale. J Maps.  https://doi.org/10.1080/17445647.2013.806229CrossRefGoogle Scholar
  12. Komac M (2006a) A earthquake events model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28CrossRefGoogle Scholar
  13. Komac M (2006b) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28CrossRefGoogle Scholar
  14. Laefer DF, Alison K, Pradhan A (2006) The need for baseline data characteristics for GIS-based disaster management systems. J Urban Plan Dev 132(3):115–119CrossRefGoogle Scholar
  15. Lillesand TM, Kiefer RW, Jonthan WC (2008) Remote sensing and image interpretation, 6th edn. Wiley, New YorkGoogle Scholar
  16. Mahalingam R, Olsen MJ (2016) Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping. Geomat Nat Hazards Risk 7(6):1835–1855CrossRefGoogle Scholar
  17. Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New YorkGoogle Scholar
  18. Malczewski J (2006) Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. Int J Appl Earth Obs Geoinf 8:270–277CrossRefGoogle Scholar
  19. Malczewski J, Chapman T, Flegel C, Walters D, Shrubsole D, Healy MA (2003) GIS-multicriteria evaluation with ordered weighted averaging (OWA): case study of developing watershed management strategies. Environ Plann A 35(10):1769–1784CrossRefGoogle Scholar
  20. Mertens KC, Verbeke LPC, Westra T, De Wulf RR (2004) Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients. Remote Sens Environ 91(2):225–236.  https://doi.org/10.1016/J.RSE.2004.03.003CrossRefGoogle Scholar
  21. Miles SB, Ho CL (1999) Applications and issues of GIS as tool for civil engineering modeling. J Comput Civil Eng ASCE 13(3):144–161CrossRefGoogle Scholar
  22. Mokarram M, Aminzadeh F (2010) GIS-based multicriteria land suitability evaluation using ordered weight averaging with fuzzy quantifier: a case study in Shavur Plain, Iran. The Int Arch Photogram Remote Sens Spat Inf Sci 38(2):508–512Google Scholar
  23. Mokarrama M, Hojati M (2016) Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM). The Egypt J Remote Sens Space SciGoogle Scholar
  24. Roustaei M, Nazi H, Amirmotallebi N (2005) The seismotectonic and zonation map of Salmas Vastness by GIS modeling according to Landsat satellite images (ETM+) and aeromagnetic data. Paper presented at the Map Middle East, 23–25 Apr 2005, Dubai, UAEGoogle Scholar
  25. Roy PS, WestenCJ VVK, Lackhera RC, Chapari ray PK (2000) Natural disasters and their mitigation-Remote Sensing and Geographical Information System Perspectives. Indian Institute of Remote Sensing Publication, DehradunGoogle Scholar
  26. Saaty TL (1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill International Book Co.Google Scholar
  27. Saaty TL, Vargas LG (1991) Prediction, Projection and Forecasting. Dordrecht: Springer Netherlands.  https://doi.org/10.1007/978-94-015-7952-0CrossRefGoogle Scholar
  28. Theilen-Willige B, Savvaidis P, Tziavos IN, Papadopoulou I (2012) Remote sensing and geographic information systems (GIS) contribution to the inventory of infrastructure susceptible to earthquake and flooding hazards in North-Eastern Greece. Geosciences 2(4):203–220CrossRefGoogle Scholar
  29. Van Westen CJ, Soeters R, Sijmons K (2000) Digital geomorphological earthquake events hazard mapping of the Alpago area, Italy. Int J Appl Earth Obs Geoinf 2:51–60CrossRefGoogle Scholar
  30. Yager RR (1988) On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190CrossRefGoogle Scholar
  31. Yagoub MM (2015) Spatio-temporal and hazard mapping of earthquake in UAE (1984–2012): remote sensing and GIS application. Geoenviron Disasters 2(1):1CrossRefGoogle Scholar
  32. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353.  https://doi.org/10.1016/S0019-9958(65)90241-Xdoi:10.1016/S0019-9958(65)90241-XCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Range and Watershed Management, College of Agriculture and Natural Resources of DarabShiraz UniversityShirazIran
  2. 2.Department of Natural Resources and Environmental Engineering, College of AgricultureShiraz UniversityShirazIran

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