single-dr.php

JDR Vol.16 No.4 pp. 571-578
(2021)
doi: 10.20965/jdr.2021.p0571

Paper:

Factoring Multi-Hazard Risk Perception in Risk Assessment and Reduction Measures in Landslide and Flash Flood Prone Areas – A Case Study of Sichon District, Nakhon Si Thammarat Province, Thailand

Indrajit Pal*,† and Jessada Karnjana**

*Disaster Preparedness, Mitigation and Management (DPMM), Asian Institute of Technology (AIT)
Moo 9, Km 42 Paholyothin Highway, Klong Luang, Pathumthani 12120, Thailand

Corresponding author

**National Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA),
Pathum Thani, Thailand

Received:
November 25, 2020
Accepted:
March 26, 2021
Published:
June 1, 2021
Keywords:
community preparedness, flash flood, landslide, multi-hazard, risk perception
Abstract

This study’s purpose is to analyze the degree of risk and vulnerability involved in landslide and flash flood prone community areas in Thepparat sub-district, Sichon district, Nakhon Si Thammarat province, Thailand. It also aims to analyze and understand the socio-economic impacts on the community at the household level, and assess the community’s risk and vulnerability by examining its risk perception. The risk perception was done using focus group discussions and a questionnaire survey with key stakeholders. It mainly focused on how the risk of landslides and flash floods influences the community’s risk perceptions, which was tested in two parts: at the organizational and community levels by focusing on government officials and households, respectively. A correlation matrix was used to understand the relationship of the indicators selected. The Pearson correlation result has shown that the degree of risk awareness positively correlates with the income level, education level, and controllability, signifying that the risk of landslides and flash floods influences household risk perceptions. The qualitative assessment recommends community-level preparedness as being paramount to reduce the risk for a resilient community.

Cite this article as:
Indrajit Pal and Jessada Karnjana, “Factoring Multi-Hazard Risk Perception in Risk Assessment and Reduction Measures in Landslide and Flash Flood Prone Areas – A Case Study of Sichon District, Nakhon Si Thammarat Province, Thailand,” J. Disaster Res., Vol.16, No.4, pp. 571-578, 2021.
Data files:
References
  1. [1] S. Paul, “Perceptions of Risk,” Science, Vol.236, No.4799, pp. 280-285, 1987.
  2. [2] S. Paul, B. Fischhoff, and S. Lichtenstein, “Rating the Risks,” Environment: Science and Policy for Sustainable Development, Vol.21, No.3, pp. 14-39. 1979.
  3. [3] I. Pal, P. Tularug, S. K. Jana, and D. K. Pal, “Risk assessment and reduction measures in landslide and flash flood-prone areas: A case of southern Thailand (Nakhon si Thammarat province),” P. Samui, D. Kim, and C. Ghosh (Eds.), “Integrating Disaster Science and Management: Global Case Studies in Mitigation and Recovery,” pp. 295-308, Elsevier, doi: 10.1016/B978-0-12-812056-9.00017-8, 2018.
  4. [4] A. Mohanty, M. Hussain, M. Mishra, D. B. Kattel, and I. Pal, “Exploring community resilience and early warning solution for flash floods, debris flow and landslides in conflict prone villages of Badakhshan, Afghanistan,” Int. J. of Disaster Risk Reduction, Vol.33, pp. 5-15, doi: 10.1016/j.ijdrr.2018.07.012, 2019.
  5. [5] K. K. Lwin, I. Pal, S. Shrestha, and P. Warnitchai, “Assessing social resilience for flood-vulnerable communities in Ayeyarwady Delta, Myanmar,” Int. J. of Disaster Risk Reduction, Vol.51, Article No.101745, doi: org/10.1016/j.ijdrr.2020.101745, 2020.
  6. [6] A. Dacosta, D. Theresa, and K. Joseph, “Risk Perception and Disaster Management in the Savannah Region of Ghana,” Int. J. of Humanities and Social Science, Vol.3, No.3, pp. 85-96, 2013.
  7. [7] K. Munjuluri, I. Pal, and N. K. Tripathi, “Geospatial Techniques for rapid Post Disaster Needs Assessment (rPDNA),” Int. J. of Recent Technology and Engineering (IJRTE), Vol.8, No.4, doi: 10.35940/ijrte.D8017.118419, 2019.
  8. [8] G. Torsten and F. Reusswig, “People at Risk of Flooding: Why Some Residents Take Precautionary Action While Others Do Not,” Natural Hazards, Vol.38, No.1-2, pp. 101-20, doi: 10.1007/s11069-005-8604-6, 2006.
  9. [9] I. Pal and S. Bhatia, “Disaster risk governance and city resilience in Asia-pacific region,” R. Shaw, K. Shiwaku, and T. Izumi (Eds.), “Science and technology in disaster risk reduction in Asia: Potentials and challenges,” pp. 137-159, doi: 10.1016/B978-0-12-812711-7.00009-2, 2017.
  10. [10] M. A. Fadlallah, I. Pal, and V. C. Hoe, “Determinants of perceived risk among artisanal gold miners: A case study of Berber locality, Sudan,” The Extractive Industries and Society, Vol.7, No.2, pp. 748-757, doi: 10.1016/j.exis.2020.03.006, 2020.
  11. [11] M. C. Ho et al., “How Do Disaster Characteristics Influence Risk Perception,” Risk Analysis, Vol.28, No.3, pp. 635-643, doi: 10.1111/j.1539-6924.2008.01040.x, 2008.
  12. [12] Defra, “Understanding the risk, empowering communities, building resilience. Department of Disaster Prevention and Mitigation. (n.d.),” Department of Disaster Prevention and Mitigation, 2011, http://www.disaster.go.th/dpm/ (in Thai) [accessed June 12, 2020]
  13. [13] Department of Mineral Resource, “Landslide Hazard map in Nakhon Si Thammarat. Environmental Research,” Thailand, 2010.
  14. [14] Thailand Integrated Water Resources Management, http://www.thaiwater.net/web/ [accessed July 23, 2020]
  15. [15] “EM-DAT (1900-2015),” EM-DAT: The International Disaster Database, http://www.emdat.be/database [accessed June 25, 2020]
  16. [16] T. Yamane, “Statistics, An Introductory Analysis,” 2nd edition, Harper and Row, 1967.
  17. [17] M.-C. Ho, D. Shaw, S. Lin, and Y.-C. Chiu, “ How Do Disaster Characteristics Influence Risk Perception?,” Risk Analysis, Vol.28, pp. 635-643, doi: 10.1111/j.1539-6924.2008.01040.x, 2008.
  18. [18] S. Ainuddin, D. P. Aldrich, J. K. Routray, S. Ainuddin, and A. Achkazai, “The need for local involvement: Decentralization of disaster management institutions in Baluchistan, Pakistan,” Int. J. of Disaster Risk Reduction, Vol.6, pp. 50-58, doi: 10.1016/j.ijdrr.2013.04.001, 2013.
  19. [19] National Statistical Office, Ministry of Information and Communication Technology, “The Household Socio-Economic Survey,” 2015.
  20. [20] I. Pal, T. Ghosh, and C. Ghosh, “Institutional framework and administrative systems for effective disaster risk governance – perspectives of 2013 cyclone Phailin in India,” Int. J. of Disaster Risk Reduction, Vol.21, pp. 350-359, doi: 10.1016/j.ijdrr.2017.01.002, 2017.
  21. [21] C. Ghosh and I. Pal, “Geotechnical measures for Uttarakhand flash flood-2013, India,” Geotechnical Engineering, Vol.48, No.1, pp. 117-127, 2017.
  22. [22] M. Kuri, “Recent Perceptions of Volcanic Hazard-Related Information in Japan: Expectation of Eruption Predictability and Acceptance of Uncertainty (2019),” J. Disaster Res., Vol.14, No.8, pp. 1072-1085, doi: 10.20965/jdr.2019.p1072, 2019.
  23. [23] H. Tanaka, D. Sasaki, and Y. Ono, “Proposed Requirement Definition Method for Developing Global Disaster Database Based on Various Means of Data Collection (2018),” J. Disaster Res., Vol.13, No.6, pp. 1015-1023, doi: 10.20965/jdr.2018.p1015, 2018.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 21, 2021