CONSTRUCTING EARTHQUAKE DISASTER-EXPOSURE LIKELIHOOD INDEX USING SHAPLEY-VALUE REGRESSION APPROACH

  • Rahma Anisa Dept. of Statistics, IPB University
  • Bagus Sartono Dept. of Statistics, IPB University
  • Pika Silvianti Dept. of Statistics, IPB University
  • Aam Alamudi Dept. of Statistics, IPB University
  • Indonesian Journal of Statistics and Its Applications IJSA
Keywords: earthquake index, java, shapley-value

Abstract

Indonesia is very prone to earthquake disaster because it is located in the Pacific ring of fire. Therefore, a reference level of earthquake disaster exposure likelihood events in Indonesia is needed in order to increase people's awareness about the risks. This study aims to determine the index that describes the risk of possible future earthquake disaster. As initial research, this study is focus on earthquake disasters in Java region, as it has the largest population in Indonesia. Several indicators that are related to the severity of earthquake disaster impact, were used in this study.  The weights of each indicators were determined by considering its shapley-value, thus all indicators gave equal contribution to the proposed index. The results showed that shapley-value approach can be utilized to construct index with equal contribution of each indicators. In general, the resulted index had similar pattern with the number of damaged houses in each districts.

References

Birkmann, J., & Welle, T. (2016). The WorldRiskIndex 2016: Reveals the necessity for regional cooperation in vulnerability reduction. Journal of Extreme Events, 3(02), 1650005.

Bivand, R., Altman, M., Anselin, L., Assunção, R., Berke, O., Bernat, A., & Blanchet, G. (2015). Package ‘spdep’. https://cran.r-project.org/web/packages/spdep/ spdep.pdf [December 6th, 2018].

Bivand, R. S., & Wong, D. W. (2018). Comparing implementations of global and local indicators of spatial association. TEST, 27(3), 716-748.

[BNPB] National Agency for Disaster Management. (2016). Indonesian Risk Disaster. BNPB, Jakarta.

Ebisudani, M., & Tokai, A. (2017). The Application of Composite Indicators to Disaster Resilience: A Case Study in Osaka Prefecture, Japan. Journal of Sustainable Development, 10(1), 81.

Grömping, U. (2006). Relative importance for linear regression in R: the package relaimpo. Journal of statistical software, 17(1), 1-27.

Mishra, S. K. (2016a). Shapley value regression and the resolution of multicollinearity. Journal of Economics Bibliography, 3(3). DOI: 10.2139/ssrn.2797224.

Mishra, S. K. (2016b). A note on construction of a composite index by optimization of Shapley value shares of the constituent variables. Turkish Economic Review, 3(3), 466-472. DOI: 10.2139/ssrn.2803798.

Parwanto, N. B., & Oyama, T. (2014). A statistical analysis and comparison of historical earthquake and tsunami disasters in Japan and Indonesia. International Journal of Disaster Risk Reduction, 7, 122-141.

Peduzzi, P., Dao, H., Herold, C., & Mouton, F. (2009). Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index. Natural Hazards and Earth System Sciences, 9(4), 1149-1159.

Syed, M. E. (2014). Attribute weighting in k-nearest neighbor classification. University of Tampere, Finland. (Master's thesis).

[U.S. Census Bureau]. 2018. Top 10 Most Populous Countries. https://www.census.gov/popclock/world [July 5th, 2018]

Welle, T., & Birkmann, J. (2015). The world risk index–an approach to assess risk and vulnerability on a global scale. Journal of Extreme Events, 2(01), 1550003.

Published
2019-02-28
Section
Articles