IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v7y2023i2p31-d1156781.html
   My bibliography  Save this article

A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of a PRED Model

Author

Listed:
  • Sara Rye

    (School of Social Sciences, Faculty of Management, Law and Social Sciences, University of Bradford, Richmond Rd., Bradford BD7 1DP, UK)

  • Emel Aktas

    (Cranfield School of Management, Cranfield University, College Road, Cranfield MK43 0AL, UK)

Abstract

Background: This paper proposes a framework to cope with the lack of data at the time of a disaster by employing predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. Methods : A panel data of 4252 natural onset disasters between 1980 to 2020 is processed through concept drift phenomenon and rule-based classifiers, namely the Moving Average (MA). Results: Predictive model for Estimating Data (PRED) is developed as a decision-making platform based on the Disaster Severity Analysis (DSA) Technique. Conclusions: comparison with the real data shows that the platform can predict the human impact of a disaster (fatality, injured, homeless) with up to 3% error; thus, it is able to inform the selection of disaster relief partners for various disaster scenarios.

Suggested Citation

  • Sara Rye & Emel Aktas, 2023. "A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of a PRED Model," Logistics, MDPI, vol. 7(2), pages 1-24, May.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:2:p:31-:d:1156781
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/7/2/31/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/7/2/31/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rawls, Carmen G. & Turnquist, Mark A., 2012. "Pre-positioning and dynamic delivery planning for short-term response following a natural disaster," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 46-54.
    2. Galateia Terti & Isabelle Ruin & Jonathan J. Gourley & Pierre Kirstetter & Zachary Flamig & Juliette Blanchet & Ami Arthur & Sandrine Anquetin, 2019. "Toward Probabilistic Prediction of Flash Flood Human Impacts," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 140-161, January.
    3. Sara Hasani & Ramzi El-Haddadeh & Emel Aktas, 2016. "The Partner Proliferation Problem in Disaster Response Networks," International Series in Operations Research & Management Science, in: Christopher W. Zobel & Nezih Altay & Mark P. Haselkorn (ed.), Advances in Managing Humanitarian Operations, chapter 6, pages 111-133, Springer.
    4. Zobel, Christopher W. & Baghersad, Milad, 2020. "Analytically comparing disaster resilience across multiple dimensions," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    5. Rivera-Royero, Daniel & Galindo, Gina & Yie-Pinedo, Ruben, 2020. "Planning the delivery of relief supplies upon the occurrence of a natural disaster while considering the assembly process of the relief kits," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    6. Savage, I. Richard, 1972. "The Significance Test Controversy—A Reader. Edited by Denton E. Morrison and Ramon E. Henkel. (Chicago: Aldine Publishing Company, 1970. Pp. xviii, 333. $12.50.)," American Political Science Review, Cambridge University Press, vol. 66(3), pages 1024-1025, September.
    7. Maharjan, Rajali & Hanaoka, Shinya, 2020. "A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    8. Abdul Sattar Safaei & Saba Farsad & Mohammad Mahdi Paydar, 2020. "Emergency logistics planning under supply risk and demand uncertainty," Operational Research, Springer, vol. 20(3), pages 1437-1460, September.
    9. Chih-Hung Pai & Yong-Ming Tien & Ta-Liang Teng, 2007. "A study of the human-fatality rate in near-fault regions using the Victim Attribute Database," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 42(1), pages 19-35, July.
    10. Li, Bin & Hernandez, Ivan & Milburn, Ashlea Bennett & Ramirez-Marquez, Jose Emmanuel, 2018. "Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 84-103.
    11. Shao, Jianfang & Liang, Changyong & Liu, Yujia & Xu, Jian & Zhao, Shuping, 2021. "Relief demand forecasting based on intuitionistic fuzzy case-based reasoning," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rodríguez-Espíndola, Oscar & Ahmadi, Hossein & Gastélum-Chavira, Diego & Ahumada-Valenzuela, Omar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel, 2023. "Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    2. Fei, Liguo & Wang, Yanqing, 2022. "Demand prediction of emergency materials using case-based reasoning extended by the Dempster-Shafer theory," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    3. Mahmoud A. Hammad & Sara Elgazzar & Borut Jereb & Marjan Sternad, 2023. "Requirements for Establishing Energy Hubs: Practical Perspective," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(64), pages 798-798, August.
    4. Souza, Juliano Silva & Lim-Apo, Flávio Araújo & Varella, Leonardo & Coelho, Antônio Sérgio & Souza, João Carlos, 2022. "Multi-period optimization model for planning people allocation in shelters and distributing aid with special constraints," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    5. A. Anaya-Arenas & J. Renaud & A. Ruiz, 2014. "Relief distribution networks: a systematic review," Annals of Operations Research, Springer, vol. 223(1), pages 53-79, December.
    6. Zobel, Christopher W. & Baghersad, Milad, 2020. "Analytically comparing disaster resilience across multiple dimensions," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    7. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    8. Melissa Gama & Bruno Filipe Santos & Maria Paola Scaparra, 2016. "A multi-period shelter location-allocation model with evacuation orders for flood disasters," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 4(3), pages 299-323, September.
    9. Jiawei Ge & Xiang Li & Zhuoling Wu & Yurou Sun & Maneerat Kanrak, 2022. "The Distribution of Emergency Logistics Centers under the COVID-19 Lockdown: The Case of Yangtze River Delta Area," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    10. Yagci Sokat, Kezban & Dolinskaya, Irina S. & Smilowitz, Karen & Bank, Ryan, 2018. "Incomplete information imputation in limited data environments with application to disaster response," European Journal of Operational Research, Elsevier, vol. 269(2), pages 466-485.
    11. Yong Xiang & Yonghua Chen & Yangyang Su & Zeyou Chen & Junna Meng, 2023. "Research on the Evaluation and Spatial–Temporal Evolution of Safe and Resilient Cities Based on Catastrophe Theory—A Case Study of Ten Regions in Western China," Sustainability, MDPI, vol. 15(12), pages 1-50, June.
    12. Eghbal Akhlaghi, Vahid & Campbell, Ann Melissa, 2022. "The two-echelon island fuel distribution problem," European Journal of Operational Research, Elsevier, vol. 302(3), pages 999-1017.
    13. Alem, Douglas & Clark, Alistair & Moreno, Alfredo, 2016. "Stochastic network models for logistics planning in disaster relief," European Journal of Operational Research, Elsevier, vol. 255(1), pages 187-206.
    14. Davis, Lauren B. & Samanlioglu, Funda & Qu, Xiuli & Root, Sarah, 2013. "Inventory planning and coordination in disaster relief efforts," International Journal of Production Economics, Elsevier, vol. 141(2), pages 561-573.
    15. Zhang, Lingye & Lu, Jing & Yang, Zaili, 2021. "Optimal scheduling of emergency resources for major maritime oil spills considering time-varying demand and transportation networks," European Journal of Operational Research, Elsevier, vol. 293(2), pages 529-546.
    16. Aaron B. Hoskins & Hugh R. Medal, 2019. "Stochastic programming solution for placement of satellite ground stations," Annals of Operations Research, Springer, vol. 283(1), pages 267-288, December.
    17. Seyed Babak Ebrahimi & Ehsan Bagheri, 2022. "A multi-objective formulation for the closed-loop plastic supply chain under uncertainty," Operational Research, Springer, vol. 22(5), pages 4725-4768, November.
    18. Esposito Amideo, A. & Scaparra, M.P. & Kotiadis, K., 2019. "Optimising shelter location and evacuation routing operations: The critical issues," European Journal of Operational Research, Elsevier, vol. 279(2), pages 279-295.
    19. Agha Iqbal Ali & Guven Ince, 2017. "Distress among disaster-affected populations: delay in relief provision," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 533-543, May.
    20. Mahya Norallahi & Hesam Seyed Kaboli, 2021. "Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 119-137, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlogis:v:7:y:2023:i:2:p:31-:d:1156781. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.