verifiedOpen Access Peer-Reviewed Scientific Journal (CC BY 4.0)
Submit Manuscript|OJS Login|Register
Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
search
person
Research ArticlesverifiedPeer-ReviewedOpen Access
Pages: 13-19

Clustering Application and Evaluation of the Countries' Word Risk and Climate Risk Indices

Nazmiye ELIGÜZEL fingerprint•
Sena AYDOĞAN fingerprint•
Ibrahim Miraç ELIGÜZEL fingerprint
Publication DateMarch 31, 2023
Volume / IssueVol. 11, No. 1 (pp. 13-19)
subject

Abstract

Societies take various initiatives to reduce the impact of natural disasters. Unfortunately, certain nations and regions are better suited than others to finding solutions to the problem, whether for political, cultural, economic, or other factors. This paper deals with the cluster analysis of 170 countries based on world risk index and climate risk index data. We use the k-means approach for clustering in sequential stages of this work. Specifically, we first carry out both the elbow method and silhouette scores to determine the number of clusters. Then clustering analysis is carried out, taking into account the World Risk Index, which includes risks of both exposure and vulnerability. Second, the Climate Risk Index is implemented into the first stage results by clustering countries after determining the number of clusters. Lastly, statistical analyses on the change of clusters for exposure, vulnerability, and climate risk are investigated and discussed in detail. Taken together, each of the risk elements like earthquake, tsunami, socioeconomic development, health care capability, etc. differs by nation. Clusters of countries with similar risks are reported. When the climate risk index is included in the evaluation, the number of clusters increases. The Climate Risk Index has been determined as a variable that cannot be ignored when countries are clustered according to their risk profiles.
Keywords:Climate risk indexClustering analysisWorld risk index

Author Affiliations

format_list_numbered

References (21)

Cited Literature
  1. 1

    D. P. Coppola, “Introduction to International Disaster Management,” Introd. to Int. Disaster Manag., 2011, doi: 10.1016/C2009-0-64027-7.

    open_in_newCrossref / DOI: 10.1016/C2009-0-64027-7
  2. 2

    RUB and IFHV, WorldRiskReport 2022 Focus: Digitalization N E W. 2022.

  3. 3

    J. Han, M. Kamber, and J. Pei, “Data Mining: Concepts and Techniques,” Data Min. Concepts Tech., 2012, doi: 10.1016/C2009-0-61819-5.

    open_in_newCrossref / DOI: 10.1016/C2009-0-61819-5
  4. 4

    G. Kou, Y. Peng, and G. Wang, “Evaluation of clustering algorithms for financial risk analysis using MCDM methods,” Inf. Sci. (Ny)., vol. 275, pp. 1–12, 2014, doi: 10.1016/j.ins.2014.02.137.

    open_in_newCrossref / DOI: 10.1016/j.ins.2014.02.137
  5. 5

    D. Horn and A. Gottlieb, “Algorithm for Data Clustering in Pattern Recognition Problems Based on Quantum Mechanics,” Phys. Rev. Lett., vol. 88, no. 1, p. 4, 2002, doi: 10.1103/PhysRevLett.88.018702.

    open_in_newCrossref / DOI: 10.1103/PhysRevLett.88.018702
  6. 6

    A. M. Mabu, R. Prasad, and R. Yadav, “Mining gene expression data using data mining techniques: A critical review,” J. Inf. Optim. Sci., vol. 41, no. 3, pp. 723–742, 2020, doi: 10.1080/02522667.2018.1555311.

    open_in_newCrossref / DOI: 10.1080/02522667.2018.1555311
  7. 7

    D. Eckstein, V. Künzel, and L. Schäfer, “Global climate risk index 2021,” Ger. e.V., p. 28, 2021, [Online]. Available: https://germanwatch.org/sites/default/files/Global Climate Risk Index 2021_2.pdf.

  8. 8

    M. Garschagen and P. Romero-Lankao, “Exploring the relationships between urbanization trends and climate change vulnerability,” Clim. Change, vol. 133, no. 1, pp. 37–52, 2015, doi: 10.1007/s10584-013-0812-6.

    open_in_newCrossref / DOI: 10.1007/s10584-013-0812-6
  9. 9

    A. Merino et al., “Large-scale patterns of daily precipitation extremes on the Iberian Peninsula,” Int. J. Climatol., vol. 36, no. 11, pp. 3873–3891, 2016, doi: 10.1002/joc.4601.

    open_in_newCrossref / DOI: 10.1002/joc.4601
  10. 10

    W. Lu, D. E. Atkinson, and N. K. Newlands, “ENSO climate risk: predicting crop yield variability and coherence using cluster-based PCA,” Model. Earth Syst. Environ., vol. 3, no. 4, pp. 1343–1359, 2017, doi: 10.1007/s40808-017-0382-0.

    open_in_newCrossref / DOI: 10.1007/s40808-017-0382-0
  11. 11

    A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks,” Comput. Commun., vol. 30, no. 14–15, pp. 2826–2841, 2007, doi: 10.1016/j.comcom.2007.05.024.

    open_in_newCrossref / DOI: 10.1016/j.comcom.2007.05.024
  12. 12

    J. B. Sheu, “An emergency logistics distribution approach for quick response to urgent relief demand in disasters,” Transp. Res. Part E Logist. Transp. Rev., vol. 43, no. 6, pp. 687–709, 2007, doi: 10.1016/j.tre.2006.04.004.

    open_in_newCrossref / DOI: 10.1016/j.tre.2006.04.004
  13. 13

    J. B. Sheu, “Dynamic relief-demand management for emergency logistics operations under large-scale disasters,” Transp. Res. Part E Logist. Transp. Rev., vol. 46, no. 1, pp. 1–17, 2010, doi: 10.1016/j.tre.2009.07.005.

    open_in_newCrossref / DOI: 10.1016/j.tre.2009.07.005
  14. 14

    H. Xu, C. Ma, J. Lian, K. Xu, and E. Chaima, “Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China,” J. Hydrol., vol. 563, no. June, pp. 975–986, 2018, doi: 10.1016/j.jhydrol.2018.06.060.

    open_in_newCrossref / DOI: 10.1016/j.jhydrol.2018.06.060
  15. 15

    K. Ali, H. X. Nguyen, Q. T. Vien, P. Shah, and Z. Chu, “Disaster Management Using D2D Communication with Power Transfer and Clustering Techniques,” IEEE Access, vol. 6, pp. 14643–14654, 2018, doi: 10.1109/ACCESS.2018.2793532.

    open_in_newCrossref / DOI: 10.1109/ACCESS.2018.2793532
  16. 16

    H. J. Chu, C. J. Liau, C. H. Lin, and B. S. Su, “Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region,” Expert Syst. Appl., vol. 39, no. 10, pp. 9451–9457, 2012, doi: 10.1016/j.eswa.2012.02.114.

    open_in_newCrossref / DOI: 10.1016/j.eswa.2012.02.114
  17. 17

    R. Oktarina and Junita, “Determine the clustering of cities in Indonesia for disaster management using K-Means by excel and RapidMiner,” IOP Conf. Ser. Earth Environ. Sci., vol. 794, no. 1, 2021, doi: 10.1088/1755-1315/794/1/012094.

    open_in_newCrossref / DOI: 10.1088/1755-1315/794/1/012094
  18. 18

    L. Morissette and S. Chartier, “The k-means clustering technique: General considerations and implementation in Mathematica,” Tutor. Quant. Methods Psychol., vol. 9, no. 1, pp. 15–24, 2013, doi: 10.20982/tqmp.09.1.p015.

    open_in_newCrossref / DOI: 10.20982/tqmp.09.1.p015
  19. 19

    E. Umargono, J. E. Suseno, and V. G. S. K., “K-Means Clustering Optimization using the Elbow Method and Early Centroid Determination Based-on Mean and Median,” vol. 474, no. Isstec 2019, pp. 234–240, 2020, doi: 10.5220/0009908402340240.

    open_in_newCrossref / DOI: 10.5220/0009908402340240
  20. 20

    F. Wang, H. H. Franco-Penya, J. D. Kelleher, J. Pugh, and R. Ross, “An analysis of the application of simplified silhouette to the evaluation of k-means clustering validity,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10358 LNAI, no. December 2018, pp. 291–305, 2017, doi: 10.1007/978-3-319-62416-7_21.

    open_in_newCrossref / DOI: 10.1007/978-3-319-62416-7_21
  21. 21

    “What is a box plot?” https://www.simplypsychology.org/boxplots.html#:~:text=What is a box plot,(or percentiles) and averages. (accessed Nov. 04, 2022).

format_quote

How to Cite This Article

N. E., S. A., I. M. E. (2023). Clustering Application and Evaluation of the Countries' Word Risk and Climate Risk Indices. International Journal of Applied Methods in Electronics and Computers, 13-19. https://doi.org/10.18100/ijamec.1217399