Seismic vulnerability assessment is a critical topic in disaster management. It is a complex uncertain spatial decision making problem due to lack of complete data, vagueness of experts’ comments in addition to uncertainties in the numerical data/relations. This paper presents a new Geospatial Information System (GIS)-based multi-criterion decision-making (MCDM) method developed for predicting building damages prior to the occurrence of a potential earthquake scenario which considers different sources of uncertainty to make realistic assessments. The developed method suggests an approximate reasoning approach through using Fuzzy Sets theory (FST) and enhanced Dempster-Shafer theory (DST). FST handles the vagueness of the heuristic knowledge on ‘importance weights of the selected criteria’ and ‘the relationship of the criteria with physical seismic vulnerability (PSV)’. The enhanced DST is used for fusion of the information by taking into account the reliability of the adopted criteria. The proposed method’s applicability is tested on existing buildings of a municipality district of Tabriz, a historical and earthquake prone city in Iran. The implementation results confirm that the proposed method is a pragmatic, rational and simple model which reduces uncertainties of PSVA to provide realistic predictions essential for assisting planners and administrators with reducing future earthquake losses in urban areas.
Meshkini, HabibiK. and AlizadehH., Using fuzzy logic and GIS tools for seismic vulnerability of old fabric in Iranian cities (Case study: Zanjan city), Journal of Intelligent and Fuzzy Systems25 (2013), 965–975.
2.
DempsterA.P., Upper and Lower Probabilities Induced by a Multivalued Mapping, 1967, pp. 325–339.
3.
AmiriA.R., DelavarM.R., ZahraiS.M. and MalekM.R., Tehran seismic vulnerability assessment using Dempster–Shafer theory of evidence, Proceedings of Map Asia Conference2007, p. 9.
4.
ATC-13, Earthquake Damage Evaluation Data for California, Applied Technology Council, Redwood City, California, 1985.
5.
ChouC.C., The canonical representation of multiplication operation on triangular fuzzy numbers, Computers & Mathematics with Applications45 (2003), 1601–1610.
6.
HwangC.L. and YoonK., Multiple attribute decision making: Methods and applications: A state-of-the-art survey, Springer-Verlag Berlin; New York, 1981.
7.
DuboisD. and PradeH., A set-theoretic view of belief functions logical operations and approximations by fuzzy sets†, International Journal of General Systems12 (1986), 193–226.
8.
DuboisD., PradeH., On the Combination of Evidence in Various Mathematical Frameworks, In: FlammJ., LuisiT. (Eds.) Reliability Data Collection and Analysis, Sringer Netherlands, Dordrecht, 1992, pp. 213–241.
9.
DuboisD. and PradeH., Possibility Theory, New York: Plenum Press, 1988.
10.
de RocquignyE., Modelling under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods, Wiley, 2012.
11.
FieldE.H., JordanT.H. and CornellC.A., OpenSHA: A developing community-modeling environment for seismic hazard analysis, Seismological Research Letters74 (2003), 406–419.
12.
LefevreE., ColotO. and VannoorenbergheP., Belief function combination and conflict management, Information Fusion3 (2002), 149–162.
13.
KhamespanahF., DelavarM.R., MoradiM. and SheikhianH., A GIS-based multi-criteria evaluation framework for uncertainty reduction in earthquake disaster management using granular computing, Geodesy and Cartography42 (2016), 58–68.
14.
RezaieF. and PanahiM., GIS modeling of seismic vulnerability of residential fabrics considering geotechnical, structural, social and physical distance indicators in Tehran using multi-criteria decision-making techniques, Natural Hazards and Earth System Sciences15 (2015), 461–474.
15.
ShaferG., A mathematical theory of evidence, Princeton University Press, Princeton, NJ, 1976.
16.
Samadi AliniaH. and DelavarM.R., Tehran’s seismic vulnerability classification using granular computing approach, Appl Geomat3 (2011), 229–240.
17.
SarvarH., AminiJ. and Laleh-PoorM., Assessment of risk caused by earthquake in region 1 of Tehran using the Combination of RADIUS, TOPSIS and AHP Models, J Civil Eng Urban1 (2011), 39–48. http://www.ojceu.ir/main/
18.
ZimmermannH.J., Fuzzy set theory–and its applications, Kluwer Academic Publishers, Boston, 1991.
19.
HAZUS Earthquake Loss Estimation Methodology— Technical and User Manuals, Federal Emergency Management Agency (FEMA), National Institute of Building Sciences, Washington, DC, Vol. 1–3, 1999.
20.
Armaş, Multi-criteria vulnerability analysis to earthquake hazard of Bucharest, Romania, Nat Hazards63 (2012), 1129–1156.
21.
Karimi, Risk management of natural disasters: A fuzzy probabilistic methodology and its application to seismic hazard, in: Bauingenieurwesen, Mainz, Aachen, 2006.
22.
RohmerJ. and BaudritC., The use of the possibility theory to investigate the epistemic uncertainties within scenario-based earthquake risk assessments, Nat Hazards56 (2011), 613–632.
23.
Hosseinzade DelirK. and Khodabakhah CharkhalooM., The study of efficiency of street networks in earthquake (Case Study of Zones 1 and 5 of Tabriz Detailed Pland), Geography and Planning18 (2015), 153–174.
24.
MualchinL., History of modern earthquake hazard mapping and assessment in california using a deterministic or scenario approach, Pure Appl Geophys168 (2011), 383–407.
25.
ZadehL.A., Fuzzy sets, Information and Control8 (1965), 338–353.
26.
ZadehL.A., Simple view of the dempster-shafer theory of evidence and its implication for the rule of combination, AI Magazine7 (1986), 85–90.
27.
ZadehL.A., The concept of a linguistic variable and its application to approximate reasoning—I, Information Sciences8 (1975), 199–249.
28.
BeynonM., CoskerD. and MarshallD., An expert system for multi-criteria decision making using Dempster Shafer theory, Expert Systems with Applications20 (2001), 357–367.
29.
HashemiM. and AlesheikhA.A., A GIS-based earthquake damage assessment and settlement methodology, Soil Dynamics and Earthquake Engineering31 (2011), 1607–1617.
30.
JahankhahM., MoshiriB., DelavarM.R. and ZareM., The evidential reasoning approach for a multi attribute decision making method in geospatial information, Journal of Control ISICE, a Joint Publication of the Iranian Society of Instrument and Control Engineers and the K.N. Toosi University of Technology3 (2009), 52–63.
31.
KamelifarM.J., RustaeiSh., AhadnejadM. and KamelifarZ., The Assessment of road network vulnerability in formal and informal (slum) urban tissues to earthquake hazards with crisis management approach (Case study: Zone 1 Tabriz), Journal of Civil Engineering and Urbanism3 (2013), 380–385.
32.
AlamM.S.N., TesfamariamS. and AlamM.S.N., GIS-based seismic damage estimation: Case study for the city of kelowna, BC, Natural Hazards Review14 (2013), 66–78.
33.
AbrahamsonN.A. and BommerJ.J., Probability and uncertainty in seismic hazard analysis, Earthquake Spectra21 (2005), 603–607.
34.
LantadaN., IrizarryJ., BarbatA.H., GoulaX., RocaA., SusagnaT. and PujadesL.G., Seismic hazard and risk scenarios for Barcelona, Spain, using the Risk-UE vulnerability index method, Bull Earthquake Eng8 (2010), 201–229.
35.
LantadaN., PujadesL. and BarbatA., Vulnerability index and capacity spectrum based methods for urban seismic risk evaluation. A comparison, Nat Hazards51 (2009), 501–524.
36.
AmbraseysN.N. and MelvilleC.P., A history of Persian earthquakes, Cambridge University Press, 1982 Translated by A. Radeh, Agah Publishers, Tehran, Iran, 1991.
37.
SmetsP., Data fusion in the transferable belief model, in: Proceedings of the Third International Conference on Information Fusion, vol. 21, 2000, pp. PS21–PS33.
38.
Tazid AliP.D. and BoruahH., A new combination rule for conflict problem of dempster-peng evidence theory, International Journal of Energy, Information and Communications3 (2012), 35–40.
39.
AghataherR., DelavarM.R., NamiM.H. and SamnayN., A fuzzy-AHP decision support system for evaluation of cities vulnerability against earthquakes, World Applied Sciences Journal3 (2008), 66–72.
40.
HassanzadehR., Nedović-BudićZ., Alavi RazaviA.NorouzzadehM. and HodhodkianH., Interactive approach for GIS-based earthquake scenario development and resource estimation (Karmania hazard model), Computers & Geosciences51 (2013), 324–338.
41.
VicenteR., ParodiS., LagomarsinoS., VarumH. and SilvaJ.A.R.M., Seismic vulnerability and risk assessment: Case study of the historic city centre of Coimbra, Portugal, Bull Earthquake Eng9 (2011), 1067–1096.
42.
YagerR.R., LiuL., Classic Works of the Dempster-Shafer Theory of Belief Functions. In: Generalizing the Demster–Shafer Theory to Fuzzy Sets, Springer, 2008, pp. 529–554.
43.
YagerR.R., Decision making under Dempster-Shafer uncertainties, International Journal of General Systems20 (1992), 233–245.
44.
YagerR.R., On the dempster-shafer framework and new combination rules, Information Sciences41 (1987), 93–137.
45.
AlimS., Application of Dempster-Shafer Theory for Interpretation of Seismic Parameters, 1988.
46.
KarimzadehS., MiyajimaM., HassanzadehR., AmiraslanzadehR. and KamelB., A GIS-based seismic hazard, building vulnerability and human loss assessment for the earthquake scenario in Tabriz, Soil Dynamics and Earthquake Engineering66 (2014), 263–280.
47.
KarimzadehS., CakirZ., OsmanoğluB., SchmalzleG., MiyajimaM., AmiraslanzadehR. and DjamourY., Interseismic strain accumulation across the North Tabriz Fault (NW Iran) deduced from InSAR time series, Journal of Geodynamics66 (2013), 53–58.
48.
LagomarsinoS. and GiovinazziS., Macroseismic and mechanical models for the vulnerability and damage assessment of current buildings, Bull Earthquake Eng4 (2006), 415–443.
49.
TyagunovS., PittoreM., WielandM., ParolaiS., BindiD., FlemingK. and ZschauJ., Uncertainty and sensitivity analyses in seismic risk assessments on the example of Cologne, Germany, Nat Hazards Earth Syst Sci14 (2014), 1625–1640.
50.
WiemerS., GiardiniD., FähD., DeichmannN. and SellamiS., Probabilistic seismic hazard assessment of Switzerland: Best estimates and uncertainties, J Seismol13 (2009), 449–478.
51.
SSHAC (Senior Seismic Hazard Analysis Committee), Recommendations for Probabilistic Seismic Hazard Analysis: Guidance on Uncertainty and Use of Experts, U.S. Nuclear Regulatory Commission, NUREG/CR-6372, Washington, DC, 1997.
52.
InagakiT., Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory, IEEE Transactions on Reliability40 (1991), 182–188.
53.
RashedT. and WeeksJ., Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas, International Journal of Geographical Information Science17 (2003), 547–576.
54.
DengY. and ChanF.T.S., A new fuzzy dempster MCDM method and its application in supplier selection, Expert Syst Appl38 (2011), 9854–9861.
55.
PengY., Regional earthquake vulnerability assessment using a combination of MCDM methods, Annals of Operations Research234 (2015), 95–110.
56.
ŞenZ., Supervised fuzzy logic modeling for building earthquake hazard assessment, Expert Systems with Applications38 (2011), 14564–14573.
