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Research Article Open access CC BY 4.0

A Statistical Analysis and Exploration on Atlantic Hurricanes Database (HURDAT2); Do We Expect the Worst?

Douglas Omwenga Nyabuga, Mukuru Ssessazi Alfred

Asian Journal of Research in Computer Science · pp. 39–57 · Published 2 Sep 2022

10.9734/ajrcos/2022/v14i330342

Abstract

Hurricane occurrence exploration is a heavy task that requires sophisticated methods to accurately determine the occurrence and the impact of damage on the tropical environs. To control massive damage, several techniques have been developed to measure the accuracy of track forecasting. The accurate analysis and exploration of the occurrence of hurricanes are very crucial for the affected environs evaluation within a short time to minimize the loss of human life and property. Nevertheless, the exact analysis and exploration of these hurricanes is challenging and time-consuming. Therefore, this study proposed a statistical model whereby the analysis of variance (ANOVA) a linear regression scientific data statistical analysis model is applied on the dataset of Atlantic hurricane database (HURDAT2) for the exploration of hurricane occurrence and damage in the tropical environs. The best tracks of a 6 hour interval, location (latitude, and longitude), wind speed, central pressure of all identified hurricanes and subtropical typhoons from the year 2008 to 2017 (10 year period) are used to determine the  R2 coefficient, which measures the goodness and fitness of the model to reveal the variability of the real data. Statistical significance and reliability of the data are tested on significance  P-value <0.05 where four different parameters are considered for the analysis in order to determine the destructive of these hurricanes in the tropical environs; latitude (x1), longitude (x2), the wind speed (x3), and central pressure. The results of our model proved significant with an accuracy of 99.3%, and a mean standard error (MSE) of 1.4952 for all the hurricanes that were analyzed, and the year 2012 was established as a year that had much damage.

Statistical analysis exploration occurrence hurricane damage tropical cyclone

References (40)

  1. 1 The Recent Increase in Atlantic Hurricane Activity: Causes and Implications [DOI]
  2. 2 Normalized Hurricane Damage in the United States: 1900–2005 [DOI]
  3. 3 US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases [DOI]
  4. 4 Assessing the present and future probability of Hurricane Harvey’s rainfall [DOI]
  5. 5 The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite-Based Method that Has Endured for over 30 Years [DOI]
  6. 6 Epidemiology of Tropical Cyclones: The Dynamics of Disaster, Disease, and Development [DOI]
  7. 7 Applied Linear Regression. [DOI]
  8. 8 Hurricane Katrina's Carbon Footprint on U.S. Gulf Coast Forests [DOI]
  9. 9 Tropical cyclones of the North Atlantic Ocean, 1871-1986.
  10. 10 Tropical cyclone disaster management using remote sensing and spatial analysis: A review [DOI]
  11. 11 Coastal inundation and damage exposure estimation: a case study for Jakarta [DOI]
  12. 12 Comparison of remote sensing change detection techniques for assessing hurricane damage to forests [DOI]
  13. 13 Hurricane risk assessment of offshore wind turbines [DOI]
  14. 14 Composite risk assessment of typhoon-induced disaster for China’s coastal area [DOI]
  15. 15 Hurricane Disaster Assessments With Image-Driven Data Mining in High-Resolution Satellite Imagery [DOI]
  16. 16 Mental Health and Recovery in the Gulf Coast After Hurricanes Katrina and Rita [DOI]
  17. 17 Global Perspectives on Tropical Cyclones [DOI]
  18. 18 Hurricane-induced sediment deposition in a gulf coast marsh [DOI]
  19. 19 A technique for the analysis and forecasting of tropical cyclone intensities from satellite pictures
  20. 20 Risk assessment on storm surges in the coastal area of Guangdong Province [DOI]
  21. 21 Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale [DOI]
  22. 22 Remote sensing assessment of forest damage in relation to the 1996 strong typhoon Herb at Lienhuachi Experimental Forest, Taiwan [DOI]
  23. 23 Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: a case study in Bangladesh [DOI]
  24. 24 Advances in research and forecasting of tropical cyclones from 1963–2013 [DOI]
  25. 25 The National Hurricane Center [DOI]
  26. 26 Digital elevation based flood hazard and vulnerability study at various return periods in Sirajganj Sadar Upazila, Bangladesh [DOI]
  27. 27 Statistical Assessment of the OWZ Tropical Cyclone Tracking Scheme in ERA-Interim [DOI]
  28. 28 Geospatial Analysis for Coastal Risk Assessment to Cyclones [DOI]
  29. 29 Deterministic and probabilistic flood modeling for contemporary and future coastal and inland precipitation inundation [DOI]
  30. 30 Tropical Cyclone Activity over the Indian Ocean in the Warmer Climate [DOI]
  31. 31 What would happen to Superstorm Sandy under the influence of a substantially warmer Atlantic Ocean? [DOI]
  32. 32 Climate Change in Tropical Cyclones and Monsoon Depressions of North Indian Ocean [DOI]
  33. 33 Characteristics of meteorological parameters associated with Hurricane Isabel [DOI]
  34. 34 Assessing the impacts of tropical cyclones
  35. 35 The 2016 North Atlantic hurricane season: A season of extremes [DOI]
  36. 36 Capabilities of Using Remote Sensing and GIS for Tropical Cyclones Forecasting, Monitoring, and Damage Assessment [DOI]
  37. 37 Emerging geo-information technologies (GIT) for natural disaster management in pakistan: an overview [DOI]
  38. 38 Estimating Extreme Losses for the Florida Public Hurricane Model [DOI]
  39. 39 Application of remote sensing for tropical cyclone track forecasting based on statistical methods [DOI]
  40. 40 Monitoring storm tide and flooding from Hurricane Sandy along the Atlantic coast of the United States, October 2012 [DOI]

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