IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v107y2021i1d10.1007_s11069-021-04620-0.html
   My bibliography  Save this article

A deep learning model for predicting climate-induced disasters

Author

Listed:
  • May Haggag

    (McMaster University)

  • Ahmad S. Siam

    (ArcelorMittal)

  • Wael El-Dakhakhni

    (McMaster University)

  • Paulin Coulibaly

    (McMaster University)

  • Elkafi Hassini

    (McMaster University)

Abstract

The increased severity and frequency of Climate-Induced Disasters (CID) including those attributed to hydrological, meteorological, and climatological effects have been testing the resilience of cities worldwide. The World Economic Forum highlighted—in its 2020 Global Risk Report—that from 2018 to 2020, three of the top five risks with respect to likelihood and impact are climate related with extreme weather events being the highest ranked risk in terms of likelihood. To alleviate the adverse impacts of CID on cities, this paper aims at predicting the occurrence of CID by linking different climate change indices to historical disaster records. In this respect, a deep learning model was developed for spatial–temporal disaster occurrence prediction. To demonstrate its application, flood disaster data from the Canadian Disaster Database was linked to climate change indices data in Ontario in order to train, test and validate the developed model. The results of the demonstration application showed that the model was able to predict flood disasters with an accuracy of around 96%. In addition to its association with precipitation indices, the study results affirm that flood disasters are closely linked to temperature-related features including the daily temperature gradient, and the number of days with minimum temperature below zero. This work introduces a new perspective in CID prediction, based on historical disaster data, global climate models, and climate change metrics, in an attempt to enhance urban resilience and mitigate CID risks on cities worldwide.

Suggested Citation

  • May Haggag & Ahmad S. Siam & Wael El-Dakhakhni & Paulin Coulibaly & Elkafi Hassini, 2021. "A deep learning model for predicting climate-induced disasters," 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. 107(1), pages 1009-1034, May.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:1:d:10.1007_s11069-021-04620-0
    DOI: 10.1007/s11069-021-04620-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-04620-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-021-04620-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Seung-Ki Min & Xuebin Zhang & Francis W. Zwiers & Gabriele C. Hegerl, 2011. "Human contribution to more-intense precipitation extremes," Nature, Nature, vol. 470(7334), pages 378-381, February.
    2. Nan, Cen & Sansavini, Giovanni, 2017. "A quantitative method for assessing resilience of interdependent infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 35-53.
    3. Michael L. Mann & James M. Warner & Arun S. Malik, 2019. "Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia," Climatic Change, Springer, vol. 154(1), pages 211-227, May.
    4. Cong, Rong-Gang & Brady, Mark, 2012. "The Interdependence between Rainfall and Temperature: Copula Analyses," MPRA Paper 112149, University Library of Munich, Germany.
    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. Davor Kvočka & Roger A. Falconer & Michaela Bray, 2016. "Flood hazard assessment for extreme flood events," 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. 84(3), pages 1569-1599, December.
    2. Ikefuji, Masako & Horii, Ryo, 2012. "Natural disasters in a two-sector model of endogenous growth," Journal of Public Economics, Elsevier, vol. 96(9-10), pages 784-796.
    3. Rei Itsukushima & Yohei Ogahara & Yuki Iwanaga & Tatsuro Sato, 2018. "Investigating the Influence of Various Stormwater Runoff Control Facilities on Runoff Control Efficiency in a Small Catchment Area," Sustainability, MDPI, vol. 10(2), pages 1-12, February.
    4. Trucco, Paolo & Petrenj, Boris, 2023. "Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Yaolong Liu & Guorui Feng & Ye Xue & Huaming Zhang & Ruoguang Wang, 2015. "Small-scale natural disaster risk scenario analysis: a case study from the town of Shuitou, Pingyang County, Wenzhou, China," 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. 75(3), pages 2167-2183, February.
    6. Islam, Moinul & Kotani, Koji & Managi, Shunsuke, 2016. "Climate perception and flood mitigation cooperation: A Bangladesh case study," Economic Analysis and Policy, Elsevier, vol. 49(C), pages 117-133.
    7. Kaustubh Salvi & Subimal Ghosh, 2016. "Projections of Extreme Dry and Wet Spells in the 21st Century India Using Stationary and Non-stationary Standardized Precipitation Indices," Climatic Change, Springer, vol. 139(3), pages 667-681, December.
    8. Licia Felicioni & Antonín Lupíšek & Petr Hájek, 2020. "Major European Stressors and Potential of Available Tools for Assessment of Urban and Buildings Resilience," Sustainability, MDPI, vol. 12(18), pages 1-27, September.
    9. Jascha Lehmann & Dim Coumou & Katja Frieler, 2015. "Increased record-breaking precipitation events under global warming," Climatic Change, Springer, vol. 132(4), pages 501-515, October.
    10. Fabian Barthel & Eric Neumayer, 2012. "A trend analysis of normalized insured damage from natural disasters," Climatic Change, Springer, vol. 113(2), pages 215-237, July.
    11. Sun, Qin & Li, Hongxu & Wang, Yuzhi & Zhang, Yingchao, 2022. "Multi-swarm-based cooperative reconfiguration model for resilient unmanned weapon system-of-systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Moglen, Rachel L. & Barth, Julius & Gupta, Shagun & Kawai, Eiji & Klise, Katherine & Leibowicz, Benjamin D., 2023. "A nexus approach to infrastructure resilience planning under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Patrick Willems, 2013. "Multidecadal oscillatory behaviour of rainfall extremes in Europe," Climatic Change, Springer, vol. 120(4), pages 931-944, October.
    14. Brennan, Timothy J., 2011. "Energy Efficiency Policy: Surveying the Puzzles," RFF Working Paper Series dp-11-27, Resources for the Future.
    15. -, 2018. "Climate Change in Central America: Potential Impacts and Public Policy Options," Sede Subregional de la CEPAL en México (Estudios e Investigaciones) 39150, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    16. Peng Jiang & Zhongbo Yu & Mahesh R. Gautam & Kumud Acharya, 2016. "The Spatiotemporal Characteristics of Extreme Precipitation Events in the Western United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4807-4821, October.
    17. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    18. Qiang Zhang & Jianfeng Li & Vijay Singh & Yungang Bai, 2012. "SPI-based evaluation of drought events in Xinjiang, China," 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. 64(1), pages 481-492, October.
    19. János Mika, 2013. "Changes in weather and climate extremes: phenomenology and empirical approaches," Climatic Change, Springer, vol. 121(1), pages 15-26, November.
    20. Thomas, Vinod & Albert, Jose Ramon G. & Perez, Rosa T., 2012. "Examination of Intense Climate-related Disasters in the Asia-Pacific," Discussion Papers DP 2012-16, Philippine Institute for Development Studies.

    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:spr:nathaz:v:107:y:2021:i:1:d:10.1007_s11069-021-04620-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.