Araştırma Makalesi
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Yıl 2023, Cilt: 41 Sayı: 2, 356 - 372, 30.04.2023

Ezgi Güler Selen Avcı Azkeskin Zerrin Aladağ

Öz

Kaynakça

  • REFERENCES
  • [1] Haq Amir, MI, Rosyidah FA, Lee GM. A formal model of the agent-based simulation for the emer-gency evacuation planning. Int J Ind Eng Theory Appl Pract 2020;27:645−664.
  • [2] Feizizadeh B, Blaschke T. Landslide risk assessment based on gıs multi-criteria evaluation: A case study in bostan-abad county, Iran. Int J Earth Sci Eng 2011;1:66−77.
  • [3] Xu C, Dai FC, Xu XW. Wenchuan earthquake-in-duced landslides: An overview. Geol Rev 2010;56:860−874.
  • [4] Kamranzad F, Memarian H, Zare M. Earthquake risk assessment for Tehran, Iran. Int J Geoinf 2020;9:1−19. [CrossRef]
  • [5] Kumlu KBY, Tudes S. Determination of earth-quake-risky areas in Yalova city center (Marmara region, Turkey) Using GIS-based multicriteria deci-sion-making techniques (Analytical hierarchy pro-cess and technique for order preference by similarity to ıdeal solution). Natur Hazards 2019;96:999−1018. [CrossRef]
  • [6] Elwood K, Filippova O, Noy I, Paz JP. Seismic policy, operations, and research uses for a building ınven-tory in an earthquake-prone city. Int J Disaster Risk Sci
  • 2020;11:709−718. [CrossRef]
  • [7] Henstra D, McBean G. Canadian disaster man-agement policy: Moving toward a paradigm shift?. Can Public Policy Anal Politiques 2005;31:303−318.[CrossRef]
  • [8] Niekerk V, Act T, Niekerk V. A critical analysis of the South African disaster management act and policy framework. Disasters 2014;38:858−877. [CrossRef]
  • [9} Paton D, Johnston D. Disasters and communities: vulnerability, resilience and preparedness. Disaster Prev 2001;10:270−277. [CrossRef]
  • [10] Chen N, Chen L, Ma Y, Chen A. Regional disaster risk assessment of China based on self-organizing map: Clustering, visualization and ranking. Int J Disaster Risk Reduct 2019;33:196−206. [CrossRef]
  • [11] Zillman J. The physical impact of disaster. In: Ingleton, J (Ed.). Natural Disaster Management. Leiceste: Tudor Rose Holding Ltd.; 1999.
  • [12] Bae YM, Lee YH. Integrated risk management pro-cess to address the problem of assigning pilot mis-sions to Korean army helicopter units. Int J Ind Eng Theory Appl Pract 2011;18:151−161.
  • [13] Shayannejad A, Angerabi BA. Earthquake vulnerabil-ity assessment in urban areas using mcdm case study: The Central part of 6 district of tehran municipality. Int Rev Spat Plan Sustain Dev 2014;2:39−51. [CrossRef]
  • [14] Peng Y. Regional earthquake vulnerability assess-ment using a combination of MCDM methods. Ann Oper Res 2015;234:95−110. [CrossRef]
  • [15] Delavar MR, Moradi M, Moshiri B. Earthquake vul-nerability assessment for hospital buildings using a gıs-based group multi criteria decision making approach: A case study of Tehran, Iran. Int Arch Photogramm Remote Sens Spat Inf Sci 2015;40:153−157. [CrossRef]
  • [16] Alizadeh M, Ngah I, Hashim M, Pradhan B, Pour AB. A hybrid analytic network process and artifi-cial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sens 2018;975:1−34. [CrossRef]
  • [17] Nyimbili PH, Erden T, Karaman H. Integration of GIS, AHP and TOPSIS for earthquake hazard analy-sis. Nat Hazards 2018;92:1523−1546. [CrossRef]
  • [18] Yariyan P, Zabihi H, Wolf ID, Karami M, Amiriyan S. Earthquake risk assessment using an ıntegrated fuzzy analytic hierarchy process with artificial neural net-works based on GIS: A case study of Sanandaj in Iran. Int J Disaster Risk Reduct 2020;50:1−17. [CrossRef]
  • [19] Jena R, Pradhan B, Beydoun G, Ardiansyah N, Sofyan H, Affan M. Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia. Geosci Front 2020;11:613−634. [CrossRef]
  • [20] Jena R, Pradhan B, Beydoun G. Earthquake vulner-ability assessment in northern sumatra province by Using a multi-criteria decision-making model. Int J Disaster Risk Reduct 2020;46:101518. [CrossRef]
  • [21] Jena R, Pradhan B, Beydoun G, Alamri A, Shanableh A. Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabi-listic neural network techniques in Odisha, India. Geocarto Int 2022;37:8080−8099. [CrossRef]
  • [22] Albulescu AC, Grozavu A, Larion D, Burghiu G. Assessing the earthquake systemic vulner-ability of the urban centres in the South-East region of Romania. The tale of Galați and Brăila Cities, Romania. Geomatics Nat Hazards Risk 2022;13:1106−1133. [CrossRef]
  • [23] Tekin B. The stock selection with ward, k-means and two-steps clustering analysis methods based on the financial ındicators. Balıkesir Univ J Soc Sci Inst 2018;21:401−436.
  • [24] Schiopu D. Applying two step cluster analysis for ıdentifying bank customers' profile. Buletinul 2010;62:66−75.
  • [25] Ceylan Z, Gursev S, Bulkan S. Evaluation of customer profile in ındividual pension sector by two-step clus-ter analysis. J Inf Technol 2017;10:475−485. [CrossRef]
  • [26] Arikan G. A Comperative Analysis of Human Development Index with Different Cluster Analysis Techniques. Master Thesis. Istanbul,Turkey: Marmara University; 2017.
  • [27] Keršuliene V, Zavadskas EK, Turskis Z. Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). J Bus Econ Manag 2010;11:243−258. [CrossRef]
  • [28] Singh RK, Modgil S. Supplier selection using SWARA and WASPAS-a case study of Indian cement industry. Meas Bus Excell 2020;24:243−265. [CrossRef]
  • [29] Hashemkhani ZS, Esfahani MH, Bitarafan M, Zavadskas EK, Arefi SL. Developing a new hybrid MCDM method for selection of the optimal alterna-tive of mechanical longitudinal ventilation of tunnel pollutants during automobile accidents. Transport 2013;28:89−96. [CrossRef]
  • [30] Yurdoglu H, Kundakci N. Server eslection with SWARA and WASPAS methods. Balıkesir Univ J Soc Sci Inst 2017;20:253−270. [CrossRef]
  • [31] Derse O, Yontar E. Determination of the most appro-prıate renewable energy source by SWARA-TOPSIS method. J Ind Eng 2020;31:389−419. [CrossRef]
  • [32] Adali EA, Isik AT. The decision making approach based on SWARA and WASPAS methods for the supplier selection problem. Int Rev Econ Manag 2017;5:56−77.
  • [33] Chatterjee P, Mondal S, Chakraborty S. A compre-hensive solution to automated inspection device selection problems using ELECTRE methods. Int J Technol 2014;2:193−208. [CrossRef]
  • [34] Yılmaz S, Fırat M, Bozkurt C, Ozdemir O. Identification of the priority regions in the cus-tomer water meters replacement using the AHP and ELECTRE methods. Sigma J Eng Nat Sci 2021;39:331−342. [CrossRef]
  • [35] Ergin A, Eker I, Alkan G. Selection of container port using ELECTRE technique. Int J Oper Log Manag 2015;4:268−275.
  • [36] AFAD. Turkey earthquake hazard map. Available at https://deprem.afad.gov.tr/deprem-tehlike-haritasi Accessed on April 23, 2021.
  • [37] Jena R, Pradhan B, Beydoun G, Alamri AM, Sofyan H. Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia. Sci Total Environ 2020;749:141582. [CrossRef]
  • [38] Jena R, Pradhan B. Integrated ANN-cross-validation and AHP-TOPSIS model to improve earth-quake risk assessment. Int J Disaster Risk Reduct 2020;50:101723. [CrossRef]
  • [39] Tate E. Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analy-sis. Nat Hazards 2012;63:325−347. [CrossRef]
  • [40] Yavasoglu F, Ozden CV. Using geographic informa-tion systems (GIS) based analytic hierarchy process (AHP) earthquake damage risk analysis: Kadikoy case. TÜBAV J Sci 2017;10:28−38.
  • [41] Murnane RJ, Daniell JE, Schäfer AM, Ward PJ, Winsemius HC, Simpson A, et al. Future scenarios for earthquake and flood risk in Eastern Europe and Central Asia. Earth's Future 2017;5:693−714. [CrossRef]
  • [42] Uyanik O. Predetermination of earthquake heavy damage areas and ımportance of macro and micro zoning for urban planning. Suleyman Demirel Univ J Nat Appl Sci 2015;19:24−38.
  • [43] TADAS. Turkish accelerometric database and anal-ysis system. Available at: https://tadas.afad.gov.tr/map Accessed on Apr 9, 2021.
  • [44] DASK. (2019). Toplumsal paylaşım etkileşimli deprem. Available at: https://dask.gov.tr/toplum-sal-paylasim-etkilesimli-deprem.html Accessed on Apr 11, 2021.
  • [45] Republic of Turkey Ministry of Industry and Technology, Total number of organized indus-trial zone and R&D centers. Available at https://www.sanayi.gov.tr/istatistikler/istatistiki-bilgiler/mi0203011502 Accessed on May 12, 2021.
  • [46] TURKSTAT. Total population. Available at: https://cip.tuik.gov.tr/ Accessed on Apr 20, 2021.
  • [47] Cakmak Z. Validity problem in clustering analysis and evaluation of clustering results. Dumlupınar University J Soc Sci 1999;3:187−205.

Earthquake risk prioritization via two-step cluster analysis and SWARA-ELECTRE methods

Yıl 2023, Cilt: 41 Sayı: 2, 356 - 372, 30.04.2023

Ezgi Güler Selen Avcı Azkeskin Zerrin Aladağ

Öz

Earthquake is one of the most destructive disasters for people, both materially and morally. Some precautions to be taken before an earthquake reduce this harmful effect. Earthquake risk assessment is one of these precautions. Earthquake risk assessment, which is an inter-disciplinary topic, is a problem suitable for clustering and multi-criteria decision-making (MCDM) techniques, as it includes more than one criterion and alternative. In this study, decision model was proposed for earthquake risk prioritization of twenty-nine provinces with high earthquake risk in Turkey. In the proposed model, provinces were clustered via Two-Step Cluster Analysis. Indicators determined in the Two-Step Cluster Analysis were defined as criteria, and criteria weighting was made via SWARA method. After weighing the criteria, the ELECTRE I method was used for earthquake risk ranking of the clustered provinces. In the proposed model for earthquake risk assessment, the similarity of provinces can be defined, and the impact of indicators can be examined. For this purpose, as the innovative aspect of this paper, while evaluating the clustering success, it was proposed to examine the coefficients of variation for continuous variables. In the Two-Step Cluster Analysis, clusters were formed in different ways and the risk rankings for provinces divided into six sub-clusters in total were presented. As a result of the Two-Step Cluster Analysis, two clusters consisting of six provinc-es, two clusters consisting of five provinces, one cluster consisting of four provinces and one cluster consisting of three provinces were obtained. The rankings of these provinces within clusters were obtained via ELECTRE I method. The aim of the study is to guide the decision makers working on earthquake risk assessment in the practical world by providing the hybrid-ization of the specified clustering and multi-criteria decision-making methods.

Anahtar Kelimeler

Earthquake Risk Prioritization ELECTRE SWARA Two-Step Cluster Analysis

Kaynakça

  • REFERENCES
  • [1] Haq Amir, MI, Rosyidah FA, Lee GM. A formal model of the agent-based simulation for the emer-gency evacuation planning. Int J Ind Eng Theory Appl Pract 2020;27:645−664.
  • [2] Feizizadeh B, Blaschke T. Landslide risk assessment based on gıs multi-criteria evaluation: A case study in bostan-abad county, Iran. Int J Earth Sci Eng 2011;1:66−77.
  • [3] Xu C, Dai FC, Xu XW. Wenchuan earthquake-in-duced landslides: An overview. Geol Rev 2010;56:860−874.
  • [4] Kamranzad F, Memarian H, Zare M. Earthquake risk assessment for Tehran, Iran. Int J Geoinf 2020;9:1−19. [CrossRef]
  • [5] Kumlu KBY, Tudes S. Determination of earth-quake-risky areas in Yalova city center (Marmara region, Turkey) Using GIS-based multicriteria deci-sion-making techniques (Analytical hierarchy pro-cess and technique for order preference by similarity to ıdeal solution). Natur Hazards 2019;96:999−1018. [CrossRef]
  • [6] Elwood K, Filippova O, Noy I, Paz JP. Seismic policy, operations, and research uses for a building ınven-tory in an earthquake-prone city. Int J Disaster Risk Sci
  • 2020;11:709−718. [CrossRef]
  • [7] Henstra D, McBean G. Canadian disaster man-agement policy: Moving toward a paradigm shift?. Can Public Policy Anal Politiques 2005;31:303−318.[CrossRef]
  • [8] Niekerk V, Act T, Niekerk V. A critical analysis of the South African disaster management act and policy framework. Disasters 2014;38:858−877. [CrossRef]
  • [9} Paton D, Johnston D. Disasters and communities: vulnerability, resilience and preparedness. Disaster Prev 2001;10:270−277. [CrossRef]
  • [10] Chen N, Chen L, Ma Y, Chen A. Regional disaster risk assessment of China based on self-organizing map: Clustering, visualization and ranking. Int J Disaster Risk Reduct 2019;33:196−206. [CrossRef]
  • [11] Zillman J. The physical impact of disaster. In: Ingleton, J (Ed.). Natural Disaster Management. Leiceste: Tudor Rose Holding Ltd.; 1999.
  • [12] Bae YM, Lee YH. Integrated risk management pro-cess to address the problem of assigning pilot mis-sions to Korean army helicopter units. Int J Ind Eng Theory Appl Pract 2011;18:151−161.
  • [13] Shayannejad A, Angerabi BA. Earthquake vulnerabil-ity assessment in urban areas using mcdm case study: The Central part of 6 district of tehran municipality. Int Rev Spat Plan Sustain Dev 2014;2:39−51. [CrossRef]
  • [14] Peng Y. Regional earthquake vulnerability assess-ment using a combination of MCDM methods. Ann Oper Res 2015;234:95−110. [CrossRef]
  • [15] Delavar MR, Moradi M, Moshiri B. Earthquake vul-nerability assessment for hospital buildings using a gıs-based group multi criteria decision making approach: A case study of Tehran, Iran. Int Arch Photogramm Remote Sens Spat Inf Sci 2015;40:153−157. [CrossRef]
  • [16] Alizadeh M, Ngah I, Hashim M, Pradhan B, Pour AB. A hybrid analytic network process and artifi-cial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sens 2018;975:1−34. [CrossRef]
  • [17] Nyimbili PH, Erden T, Karaman H. Integration of GIS, AHP and TOPSIS for earthquake hazard analy-sis. Nat Hazards 2018;92:1523−1546. [CrossRef]
  • [18] Yariyan P, Zabihi H, Wolf ID, Karami M, Amiriyan S. Earthquake risk assessment using an ıntegrated fuzzy analytic hierarchy process with artificial neural net-works based on GIS: A case study of Sanandaj in Iran. Int J Disaster Risk Reduct 2020;50:1−17. [CrossRef]
  • [19] Jena R, Pradhan B, Beydoun G, Ardiansyah N, Sofyan H, Affan M. Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia. Geosci Front 2020;11:613−634. [CrossRef]
  • [20] Jena R, Pradhan B, Beydoun G. Earthquake vulner-ability assessment in northern sumatra province by Using a multi-criteria decision-making model. Int J Disaster Risk Reduct 2020;46:101518. [CrossRef]
  • [21] Jena R, Pradhan B, Beydoun G, Alamri A, Shanableh A. Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabi-listic neural network techniques in Odisha, India. Geocarto Int 2022;37:8080−8099. [CrossRef]
  • [22] Albulescu AC, Grozavu A, Larion D, Burghiu G. Assessing the earthquake systemic vulner-ability of the urban centres in the South-East region of Romania. The tale of Galați and Brăila Cities, Romania. Geomatics Nat Hazards Risk 2022;13:1106−1133. [CrossRef]
  • [23] Tekin B. The stock selection with ward, k-means and two-steps clustering analysis methods based on the financial ındicators. Balıkesir Univ J Soc Sci Inst 2018;21:401−436.
  • [24] Schiopu D. Applying two step cluster analysis for ıdentifying bank customers' profile. Buletinul 2010;62:66−75.
  • [25] Ceylan Z, Gursev S, Bulkan S. Evaluation of customer profile in ındividual pension sector by two-step clus-ter analysis. J Inf Technol 2017;10:475−485. [CrossRef]
  • [26] Arikan G. A Comperative Analysis of Human Development Index with Different Cluster Analysis Techniques. Master Thesis. Istanbul,Turkey: Marmara University; 2017.
  • [27] Keršuliene V, Zavadskas EK, Turskis Z. Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). J Bus Econ Manag 2010;11:243−258. [CrossRef]
  • [28] Singh RK, Modgil S. Supplier selection using SWARA and WASPAS-a case study of Indian cement industry. Meas Bus Excell 2020;24:243−265. [CrossRef]
  • [29] Hashemkhani ZS, Esfahani MH, Bitarafan M, Zavadskas EK, Arefi SL. Developing a new hybrid MCDM method for selection of the optimal alterna-tive of mechanical longitudinal ventilation of tunnel pollutants during automobile accidents. Transport 2013;28:89−96. [CrossRef]
  • [30] Yurdoglu H, Kundakci N. Server eslection with SWARA and WASPAS methods. Balıkesir Univ J Soc Sci Inst 2017;20:253−270. [CrossRef]
  • [31] Derse O, Yontar E. Determination of the most appro-prıate renewable energy source by SWARA-TOPSIS method. J Ind Eng 2020;31:389−419. [CrossRef]
  • [32] Adali EA, Isik AT. The decision making approach based on SWARA and WASPAS methods for the supplier selection problem. Int Rev Econ Manag 2017;5:56−77.
  • [33] Chatterjee P, Mondal S, Chakraborty S. A compre-hensive solution to automated inspection device selection problems using ELECTRE methods. Int J Technol 2014;2:193−208. [CrossRef]
  • [34] Yılmaz S, Fırat M, Bozkurt C, Ozdemir O. Identification of the priority regions in the cus-tomer water meters replacement using the AHP and ELECTRE methods. Sigma J Eng Nat Sci 2021;39:331−342. [CrossRef]
  • [35] Ergin A, Eker I, Alkan G. Selection of container port using ELECTRE technique. Int J Oper Log Manag 2015;4:268−275.
  • [36] AFAD. Turkey earthquake hazard map. Available at https://deprem.afad.gov.tr/deprem-tehlike-haritasi Accessed on April 23, 2021.
  • [37] Jena R, Pradhan B, Beydoun G, Alamri AM, Sofyan H. Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia. Sci Total Environ 2020;749:141582. [CrossRef]
  • [38] Jena R, Pradhan B. Integrated ANN-cross-validation and AHP-TOPSIS model to improve earth-quake risk assessment. Int J Disaster Risk Reduct 2020;50:101723. [CrossRef]
  • [39] Tate E. Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analy-sis. Nat Hazards 2012;63:325−347. [CrossRef]
  • [40] Yavasoglu F, Ozden CV. Using geographic informa-tion systems (GIS) based analytic hierarchy process (AHP) earthquake damage risk analysis: Kadikoy case. TÜBAV J Sci 2017;10:28−38.
  • [41] Murnane RJ, Daniell JE, Schäfer AM, Ward PJ, Winsemius HC, Simpson A, et al. Future scenarios for earthquake and flood risk in Eastern Europe and Central Asia. Earth's Future 2017;5:693−714. [CrossRef]
  • [42] Uyanik O. Predetermination of earthquake heavy damage areas and ımportance of macro and micro zoning for urban planning. Suleyman Demirel Univ J Nat Appl Sci 2015;19:24−38.
  • [43] TADAS. Turkish accelerometric database and anal-ysis system. Available at: https://tadas.afad.gov.tr/map Accessed on Apr 9, 2021.
  • [44] DASK. (2019). Toplumsal paylaşım etkileşimli deprem. Available at: https://dask.gov.tr/toplum-sal-paylasim-etkilesimli-deprem.html Accessed on Apr 11, 2021.
  • [45] Republic of Turkey Ministry of Industry and Technology, Total number of organized indus-trial zone and R&D centers. Available at https://www.sanayi.gov.tr/istatistikler/istatistiki-bilgiler/mi0203011502 Accessed on May 12, 2021.
  • [46] TURKSTAT. Total population. Available at: https://cip.tuik.gov.tr/ Accessed on Apr 20, 2021.
  • [47] Cakmak Z. Validity problem in clustering analysis and evaluation of clustering results. Dumlupınar University J Soc Sci 1999;3:187−205.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapısal Biyoloji
Bölüm Research Articles
Yazarlar

Ezgi Güler 0000-0001-8789-8244

Selen Avcı Azkeskin 0000-0001-7433-5696

Zerrin Aladağ 0000-0002-5986-7210

Yayımlanma Tarihi 30 Nisan 2023
Gönderilme Tarihi 16 Şubat 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 41 Sayı: 2

Kaynak Göster

Vancouver Güler E, Avcı Azkeskin S, Aladağ Z. Earthquake risk prioritization via two-step cluster analysis and SWARA-ELECTRE methods. SIGMA. 2023;41(2):356-72.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/