Skip to main content

Post-disaster Need Assessment

  • Chapter
  • First Online:
Reliable Post Disaster Services over Smartphone Based DTN

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 137))

Abstract

Post-disaster need assessment deals with the accurate assessment of the need (i.e. demand and utility ) for emergency resource at the shelters. While demand signifies the amount of resource required, utility represents the exigency of that requirement. Due to lack of, or imprecise need assessments immediately after a disaster , relief requirements are generally set up based on coarse estimates by logisticians regarding what people would normally need. The effectiveness of this estimation depends on the competencies and experience of the logistician in control, often leading to impromptu allocation of typically scarce emergency resources. Thus, forecasting the exact demand and enumerating the correct utility of emergency resources are inevitable.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Guo J, Zhou G (2011) Research on emergency material demand forecast method under large-scale earthquake. Value Eng 2011(22):27–29

    Google Scholar 

  2. Spencer S et al (2009) A multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inform 42(1):123–139

    Article  Google Scholar 

  3. Meng C (2007) The research on emergency supplies inventory management based on fuzzy evaluation and gray neural network. Master’s Thesis, Wuhan University of Technology, Hubei Sheng, China

    Google Scholar 

  4. Sheu J (2007) An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transp Res Part E Logistics Transp Rev 43(6):687–709

    Article  Google Scholar 

  5. Sun B, Maa W, Zhao H (2013) A fuzzy rough set approach to emergency material demand prediction over two universes. Appl Math Model 37(11–12):7062–7070

    Article  MathSciNet  Google Scholar 

  6. Zhang H, Xu J (2010) Research on Emergency Material Demand Forecasting Model in Disaster Based on MLR–CBR. In: Proceedings of international conference of logistics engineering and management 2010, pp 387–404

    Google Scholar 

  7. Xiao W, Ya-ming Z (2011) Forecasting model of unconventional emergence incident’s resource demand based on case-based reasoning. J. Xidian Univ (Social Science Edition) 2010(4)

    Google Scholar 

  8. Yu CH (2011) Principal component regression as a countermeasure against collinearity. In: Proceedings of Western SAS software users conference 2011, pp 1–7

    Google Scholar 

  9. Massy WF (1965) Principal components regression in exploratory statistical research. J Am Stat Assoc 60(309):234–256

    Article  Google Scholar 

  10. Haque MM, Rahman A, Hagare D, Kibria G (2013) Principal component regression analysis in water demand forecasting. J Hydrol Environ Res 1(1):49–59

    Google Scholar 

  11. Saravanan S, Kannan S, Thangaraj C (2012) India’s electricity demand forecast using regression analysis and artificial neural networks based on principal components. ICTACT J Soft Comput 2(4):365–370

    Article  Google Scholar 

  12. Ismail NA, Abdullah SM (2016) Principal component regression with artificial neural network to improve prediction of in electricity demand. Int Arab J Inf Technol 13(1A):196–202

    Google Scholar 

  13. Rajab JM, MatJafri M, Lim H (2013) Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia. Atmos Environ 71:36–43

    Article  Google Scholar 

  14. Sousa S, Martins F, Alvim-Ferraz M, Pereira M (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Model Softw 22(1):97–103

    Article  Google Scholar 

  15. Florez JV, Lauras M, Dupont L, Charles A (2013) Towards a demand forecast methodology for recurrent disasters. WIT Trans Built Environ 133:99–110

    Article  Google Scholar 

  16. Basu S, Roy S, DasBit S (2018) A post disaster demand forecasting system using principal component regression analysis and case-based reasoning over smartphone based DTN. IEEE Trans Eng Manage https://doi.org/10.1109/tem.2018.2794146

  17. Guidelines for Estimating Food and Nutritional Needs in Emergencies (1997) World food programme. Available via http://www.unhcr.org/publications/operations/3b9cbef7a/wfpunhcr-guidelines-estimating-food-nutritional-needs-emergencies.html. Accessed July 2016

  18. Food and Nutrition in Disasters Guidelines (2014) PAHO/WHO INCAP. Available via http://www.paho.org/disasters/index.php?option=com_content&view=article&id=553%3Anutrition-and-food-safety-in-emergency-situations-incap&Itemid=663&lang=en

  19. Rapid Joint Needs Assessment Format (2014) Inter Agency Group. Available via http://iagodisha.org.in/. Accessed July 2016

  20. Emergency Food Security Assessment Handbook (2005) World food programme. Available via http://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp142691.pdf. Accessed July 2016

  21. RAPID Needs Assessment Format (2016) Available via https://sphereindiablog.files.wordpress.com/2016/08/jrna-village-tool-revised1.pdf. Accessed April 2015

  22. The Sphere Project (2015). Available via http://www.sphereproject.org/. Accessed April 2015

  23. Benfield A (2015) Nepal earthquake event recap report. Available via http://thoughtleadership.aonbenfield.com/documents/201509-nepal-earthquake.pdf. Accessed July 2016

  24. Google Map of Water, Food, Shelter and Medical Resources for Nepal Earthquake (2015) Available via https://www.google.com/maps/d/viewer?mid=1Iv7GILViqyJAFn5o5hi1F2Fg8mc&hl=en_US. Accessed July 2016

  25. Nepal Disaster Risk Reduction Portal, Government of Nepal (2015) Available via http://drrportal.gov.np/distributed_country. Accessed July 2016

  26. Nepal Earthquake Recovery Monitoring Assessment (2015) Shelter Cluster Nepal. https://www.sheltercluster.org/sites/default/files/docs/reach_npl_report_shelter_recovery_monitoring_assessment_nov2015.pdf. Accessed July 2016

  27. XLSTAT (2015) Statistical software for Microsoft Excel. Available via https://www.xlstat.com/en/. Accessed July 2016

  28. Basu S, Roy S, Bandyopadhyay S, DasBit S (2018) A utility driven post disaster emergency resource allocation system using DTN. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/tsmc.2018.2813008

  29. Friedman DD (1986) The consumer: marginal value, marginal utility, and consumer surplus. In: Price theory: an intermediate text, South-Western Publishing Co (Chapter 4)

    Google Scholar 

  30. Krugman P, Wells R (2009) The rational consumer. In: Microeconomics. Worth Publishers, pp 269–290

    Google Scholar 

  31. Utility of Clams (2016) Available via http://sandovalhernandezj.people.cofc.edu/index_files/ch10.pdf. Accessed Jan 2016

  32. Kuo WH, Liao W (2005) Utility-based optimal resource allocation in wireless networks. In: Proceedings of GLOBECOM 2005, pp 3408–3512

    Google Scholar 

  33. McConnell C, Brue S, Flynn S (2014) Law of diminishing marginal utility. In: Economics: principles, problems, & policies. McGraw-Hill Series in Economics, pp 134–153

    Google Scholar 

  34. Gershuny J (2009) activities, durations and the empirical estimation of utility. In: Sociology Working Papers, University of Oxford

    Google Scholar 

  35. Total Utility vs. Marginal Utility. Available via http://www.yourarticlelibrary.com/managerial-economics/total-utility-vs-marginal-utility-explained-with-diagram/28383/. Accessed Jan 2016

  36. Rittenberg L, Tregarthen T (2016) The concept of utility. In: Principles of microeconomics, v. 1.0. Available via http://catalog.flatworldknowledge.com/bookhub/21?e=rittenberg-ch07_s02. Accessed Jan 2016

  37. Total Utility (2016) AmosWEB Encyclonomic WEBopedia. Available via http://www.AmosWEB.com. Accessed Jan 2016

  38. Hayes A (2016) Economics basics: utility. Investopedia Academy. Available via http://www.investopedia.com/university/economics/economics5.asp. Accessed Jan 2016

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Souvik Basu .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Basu, S., Roy, S., Das Bit, S. (2019). Post-disaster Need Assessment. In: Reliable Post Disaster Services over Smartphone Based DTN. Smart Innovation, Systems and Technologies, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-13-6573-7_2

Download citation

Publish with us

Policies and ethics