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Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal

Email: aml@fe.up.pt

Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email: jzzhang@mail.xjtu.edu.cn


Predicting the Severity of Tornado Events by Learning a Statistical Manifold for Tornado Property Losses

Journal of Environmental Accounting and Management 12(2) (2024) 129--139 | DOI:10.5890/JEAM.2024.06.002

Thilini V. Mahanama$^1$, Pushpi Paranamana$^2$, Dimitri Volchenkov$^3$

$^1$ Department of Industrial Management, Faculty of Science, University of Kelaniya, Kelaniya, 11600, Sri Lanka

$^2$ Department of Mathematics & Computer Science, Saint Mary's College, IN 46556, USA

$^3$ Department of Mathematics & Statistics, Texas Tech University, Lubbock TX 79409-1042, USA

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Abstract

We examine the relationship between property losses caused by tornadoes and their physical parameters, namely the tornado path length and width, using data reported by the % Database National Oceanic and Atmospheric Administration in the United States.~We observe that the statistics of property losses cannot be described by a single distribution % Our solution: learn two-dimensional statistical manifold but rather by a two-dimensional statistical manifold of distributions that may reflect two different mechanisms of property loss compensations. % Methods Assessing the difference between distributions of losses caused by tornadoes using Kolmogorov-Smirnov's distance, we construct the 2-D manifold using the method of multi-dimensional scaling. % Results Then we define a “curvature coefficient” that characterizes the contraction and expansion of the derived manifold to explain the complex dynamics of the probability distributions of losses. % Conclusions: Extreme Events The regions with expansions identify the ranges of physical parameters for which the extreme tornado events may occur, which helps in assessing compensation strategies.

References

  1. [1]  Ij, H. (2018), Statistics versus machine learning, Nat Methods, 15(4), 233.
  2. [2]  Ley, C., Martin, R.K., Pareek, A., Groll, A., Seil, R., and Tischer, T. (2022), Machine learning and conventional statistics: making sense of the differences, Knee Surgery, Sports Traumatology, Arthroscopy, 30(3), 1-5.
  3. [3]  Cayton, L. (2005), Algorithms for manifold learning. Univ. of California at San Diego Tech. Rep, 12(1-17), 1.
  4. [4]  Fefferman, C., Mitter, S., and Narayanan, H. (2016), Testing the manifold hypothesis, Journal of the American Mathematical Society, 29(4), 983-1049.
  5. [5]  Sritharan, D., Wang, S., and Hormoz, S. (2021), Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry, Proceedings of the National Academy of Sciences, 118(29), e2100473118.
  6. [6]  Rathnayake, K., Lebedev, A., and Volchenkov, D. (2022), Deciding on a continuum of equivalent alternatives engaging uncertainty through behavior patterning, Foundations, 2(4), 1080-1100.
  7. [7]  Shelmerdine, S.C., Arthurs, O.J., Denniston, A., and Sebire, N.J. (2021), Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare, BMJ Health \& Care Informatics, 28(1).
  8. [8]  Beam, A.L., and Kohane, I.S. (2018), Big data and machine learning in health care, Jama, 319(13), 1317-1318.
  9. [9]  Nicholas, N. (2008), The black swan: the impact of the highly improbable, Journal of the Management Training Institut, 36(3), 56.
  10. [10]  Daneshvaran, S. and Morden, R.E. (2007), Tornado risk analysis in the United States, The Journal of Risk Finance.
  11. [11]  Ericson, R., Barry, D., and Doyle, A. (2000), The moral hazards of neo-liberalism: lessons from the private insurance industry, Economy and society, 29(4), 532-558.
  12. [12]  Peng, X., Roueche, D.B., Prevatt, D.O., and Gurley, K.R. (2016), An engineering-based approach to predict tornado-induced damage, In Multi-Hazard Approaches to Civil Infrastructure Engineering, Springer, Cham. 311-335.
  13. [13]  Lindsay, B.R., Kapp, L., Shields, D.A., Stubbs, M., Lister, S.A., McCarty, M., et al (2012), Federal emergency management: A brief introduction. Library of congress Washington DC.
  14. [14]  Choi, J. and Wehde, W. (2020), Trust in emergency management authorities and individual emergency preparedness for tornadoes, Risk, Hazards \& Crisis in Public Policy, 11(1), 12-34.
  15. [15]  Kapucu, N., Augustin, M.E., and Garayev, V. (2009), Interstate partnerships in emergency management: Emergency management assistance compact in response to catastrophic disasters, Public Administration Review, 69(2), 297-313.
  16. [16]  Greenough, G., McGeehin, M., Bernard, S.M., Trtanj, J., Riad, J., and Engelberg, D. (2001), The potential impacts of climate variability and change on health impacts of extreme weather events in the United States, Environmental health perspectives, 109(2), 191-198.
  17. [17]  NWS. (2019), The Enhanced Fujita Scale (EF Scale), Storm Prediction Center, National Weather Service.
  18. [18]  Fujita, T.T. (1971), Proposed characterization of tornadoes and hurricanes by area and intensity (No. NASA-CR-125545).
  19. [19]  Mahanama, T. and Volchenkov, D. (2022), Tornado Property Loss Scale: Up to \$8 Billion by 2025. Classification, Dependence, and Prediction of Tornado Events in the US. Journal of Environmental Accounting and Management, 10(02), 127-142.
  20. [20]  Doswell III, C.A., and Burgess, D.W. (1988), On some issues of United States tornado climatology, Monthly Weather Review, 116(2), 495-501.
  21. [21]  Brooks, H.E. (2004), On the relationship of tornado path length and width to intensity, Weather and Forecasting, 19(2), 310-319.
  22. [22]  Elsner, J.B., Jagger, T.H., and Elsner, I.J. (2014), Tornado intensity estimated from damage path dimensions, Plos One, 9(9), e107571.
  23. [23]  Strader, S.M., Ashley, W., Irizarry, A., and Hall, S. (2015), A climatology of tornado intensity assessments. Meteorological Applications, 22(3), 513-524.
  24. [24]  NWS. (2019), National Weather Service Reports.
  25. [25]  NCEI. (2018), Storm Events Database. National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration.
  26. [26]  Nelsen, R.B. (1991), Copulas and association. In Advances in Probability Distributions with given Marginals, Springer, Dordrecht. 51-74.
  27. [27]  Coleman, T.A., and Dixon, P.G. (2014), An objective analysis of tornado risk in the United States, Weather and Forecasting, 29(2), 366-376.
  28. [28]  Schneider, S.K. (1998), Reinventing public administration: a case study of the federal emergency management agency, Public Administration Quarterly, 35-57.
  29. [29]  Massey Jr, F.J. (1951), The Kolmogorov-Smirnov test for goodness of fit, Journal of the American statistical Association, 46(253), 68-78.
  30. [30]  Borg, I. and Groenen, P. J. (2005), Modern Multidimensional Scaling: Theory and Applications, Springer Science \& Business Media.
  31. [31]  Serva, M., Vergni, D., Volchenkov, D., and Vulpiani, A. (2017), Recovering geography from a matrix of genetic distances, Europhysics Letters, 118(4), 48003.
  32. [32]  Heuser, A., Rioul, O., and Guilley, S. (2014), A theoretical study of Kolmogorov-Smirnov distinguishers. In International Workshop on Constructive Side-Channel Analysis and Secure Design, Springer, Cham. 9-28.
  33. [33]  Chen, C.P. and Zhang, C.Y. (2014), Data-intensive applications, challenges, techniques and technologies: A survey on big data, Information sciences, 275, 314-347.
  34. [34]  Mead, A. (1992), Review of the development of multidimensional scaling methods, Journal of the Royal Statistical Society: Series D (The Statistician), 41(1), 27-39.
  35. [35]  Birchfield, S.T. and Subramanya, A. (2005), Microphone array position calibration by basis-point classical multidimensional scaling, IEEE transactions on Speech and Audio Processing, 13(5), 1025-1034.
  36. [36]  Wickelmaier, F. (2003), An introduction to MDS. Sound Quality Research Unit, Aalborg University, Denmark, 46(5), 1-26.
  37. [37]  Lee, E.T. (1989), Choosing nodes in parametric curve interpolation, Computer-Aided Design, 21(6), 363-370.