IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v116y2023i3d10.1007_s11069-023-05845-x.html
   My bibliography  Save this article

Environmental, climatic, and situational factors influencing the probability of fatality or injury occurrence in flash flooding: a rare event logistic regression predictive model

Author

Listed:
  • Shi Chang

    (Texas A&M University)

  • Rohan Singh Wilkho

    (Texas A&M University)

  • Nasir Gharaibeh

    (Texas A&M University)

  • Garett Sansom

    (Texas A&M University)

  • Michelle Meyer

    (Texas A&M University)

  • Francisco Olivera

    (Texas A&M University)

  • Lei Zou

    (Texas A&M University)

Abstract

Flash flooding is considered one of the most lethal natural hazards in the USA as measured by the ratio of fatalities to people affected. However, the occurrence of injuries and fatalities during flash flooding was found to be rare (about 2% occurrence rate) based on our analysis of 6,065 flash flood events that occurred in Texas over a 15-year period (2005 to 2019). This article identifies climatic, environmental, and situational factors that affect the occurrence of fatalities and injuries in flash flood events and provides a predictive model to estimate the likelihood of these occurrences. Due to the highly imbalanced dataset, three forms of logit models were investigated to achieve unbiased estimations of the model coefficients. The rare event logistic regression (Relogit) model was found to be the most suitable model. The model considers ten independent situational, climatic, and environmental variables that could affect human safety in flash flood events. Vehicle-related activities during flash flooding exhibited the greatest effect on the probability of human harm occurrence, followed by the event’s time (daytime vs. nighttime), precipitation amount, location with respect to the flash flood alley, median age of structures in the community, low water crossing density, and event duration. The application of the developed model as a simulation tool for informing flash flood mitigation planning was demonstrated in two study cases in Texas.

Suggested Citation

  • Shi Chang & Rohan Singh Wilkho & Nasir Gharaibeh & Garett Sansom & Michelle Meyer & Francisco Olivera & Lei Zou, 2023. "Environmental, climatic, and situational factors influencing the probability of fatality or injury occurrence in flash flooding: a rare event logistic regression predictive model," 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. 116(3), pages 3957-3978, April.
  • Handle: RePEc:spr:nathaz:v:116:y:2023:i:3:d:10.1007_s11069-023-05845-x
    DOI: 10.1007/s11069-023-05845-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-05845-x
    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/s11069-023-05845-x?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. Imbens, Guido W, 1992. "An Efficient Method of Moments Estimator for Discrete Choice Models with Choice-Based Sampling," Econometrica, Econometric Society, vol. 60(5), pages 1187-1214, September.
    2. Lancaster, Tony & Imbens, Guido, 1996. "Case-control studies with contaminated controls," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 145-160.
    3. Michalis Diakakis, 2020. "Types of Behavior of Flood Victims around Floodwaters. Correlation with Situational and Demographic Factors," Sustainability, MDPI, vol. 12(11), pages 1-17, May.
    4. Bull, Shelley B. & Mak, Carmen & Greenwood, Celia M. T., 2002. "A modified score function estimator for multinomial logistic regression in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 57-74, March.
    5. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    6. Gao, Sujuan & Shen, Jianzhao, 2007. "Asymptotic properties of a double penalized maximum likelihood estimator in logistic regression," Statistics & Probability Letters, Elsevier, vol. 77(9), pages 925-930, May.
    7. 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.
    8. Cosslett, Stephen R, 1981. "Maximum Likelihood Estimator for Choice-Based Samples," Econometrica, Econometric Society, vol. 49(5), pages 1289-1316, September.
    9. Galateia Terti & Isabelle Ruin & Sandrine Anquetin & Jonathan Gourley, 2015. "Dynamic vulnerability factors for impact-based flash flood prediction," 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. 79(3), pages 1481-1497, December.
    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. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    2. Adam M. Kleinbaum & Toby E. Stuart & Michael L. Tushman, 2013. "Discretion Within Constraint: Homophily and Structure in a Formal Organization," Organization Science, INFORMS, vol. 24(5), pages 1316-1336, October.
    3. Butler, J. S., 2000. "Efficiency results of MLE and GMM estimation with sampling weights," Journal of Econometrics, Elsevier, vol. 96(1), pages 25-37, May.
    4. Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(1), pages 343-364.
    5. Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers 07/03, Institute for Fiscal Studies.
    6. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Nail Kashaev, 2022. "Estimation of Parametric Binary Outcome Models with Degenerate Pure Choice-Based Data with Application to COVID-19-Positive Tests from British Columbia," University of Western Ontario, Departmental Research Report Series 20225, University of Western Ontario, Department of Economics.
    8. Esmeralda Ramalho, 2004. "Covariate Measurement Error in Endogenous Stratified Samples," Economics Working Papers 2_2004, University of Évora, Department of Economics (Portugal).
    9. Daniel McFadden, 2001. "Economic Choices," American Economic Review, American Economic Association, vol. 91(3), pages 351-378, June.
    10. Prokhorov, Artem & Schmidt, Peter, 2009. "GMM redundancy results for general missing data problems," Journal of Econometrics, Elsevier, vol. 151(1), pages 47-55, July.
    11. Lee, Kangbok & Joo, Sunghoon & Baik, Hyeoncheol & Han, Sumin & In, Joonhwan, 2020. "Unbalanced data, type II error, and nonlinearity in predicting M&A failure," Journal of Business Research, Elsevier, vol. 109(C), pages 271-287.
    12. Wonsang Ryu & Jeffrey J. Reuer & Thomas H. Brush, 2020. "The effects of multimarket contact on partner selection for technology cooperation," Strategic Management Journal, Wiley Blackwell, vol. 41(2), pages 267-289, February.
    13. Kyungchul Song, 2009. "Efficient Estimation of Average Treatment Effects under Treatment-Based Sampling," PIER Working Paper Archive 09-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    14. Gonzalez, Pedro & Vasconcellos, Geraldo M. & Kish, Richard J., 1998. "Cross-border mergers and acquisitions: The undervaluation hypothesis," The Quarterly Review of Economics and Finance, Elsevier, vol. 38(1), pages 25-45.
    15. Esmeralda A. Ramalho & Joaquim J. S. Ramalho, 2006. "Two‐Step Empirical Likelihood Estimation Under Stratified Sampling When Aggregate Information Is Available," Manchester School, University of Manchester, vol. 74(5), pages 577-592, September.
    16. Imbens, Guido W. & Lancaster, Tony, 1996. "Efficient estimation and stratified sampling," Journal of Econometrics, Elsevier, vol. 74(2), pages 289-318, October.
    17. Robert Rosenman & Scott Goates & Laura Hill, 2012. "Participation in universal prevention programmes," Applied Economics, Taylor & Francis Journals, vol. 44(2), pages 219-228, January.
    18. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 1053-1079.
    19. Shi, Mengze & Yang, Botao & Chiang, Jeongwen, 2018. "Dyad Calling Behavior: Asymmetric Power and Tie Strength Dynamics," Journal of Interactive Marketing, Elsevier, vol. 42(C), pages 63-79.
    20. Ramalho, Esmeralda A., 2002. "Regression models for choice-based samples with misclassification in the response variable," Journal of Econometrics, Elsevier, vol. 106(1), pages 171-201, January.

    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:116:y:2023:i:3:d:10.1007_s11069-023-05845-x. 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.