Modelling Wind Speed at Ikeja Station using Skewed Statistical Distributions

Olalude, G. A.; Afolabi, S. A.; Olaleye, O. A.; Folorunso, S. A.

doi:10.64060/JASR.v2i1.1

Authors

Olalude, G. A. Department of Statistics, Federal Polytechnic, Ede, Nigeria Author https://orcid.org/0000-0002-9950-0552
Afolabi, S. A. Department of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia Author https://orcid.org/0000-0002-5492-2938
Olaleye, O. A. Department of Statistics, Federal Polytechnic, Ede, Nigeria Author
Folorunso, S. A. School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK Author https://orcid.org/0000-0002-0232-4389

DOI:

https://doi.org/10.64060/JASR.v2i1.1

Keywords:

Climate changee, Data visualization, Meteorologists, Precautionary measures, Temperature variations

Abstract

Wind speed, a key atmospheric parameter, results from the movement of air from high- to low-pressure regions driven primarily by temperature variations. This study modelled the wind speed (m/s) of Ikeja, Lagos State, using data from 2000 to 2020 through statistical techniques such as descriptive statistics, data visualization, and goodness-of-fit (GOF) tests, including chi-square, Kolmogorov-Smirnov, Anderson-Darling, and Cramer-von Mises analysis. Three positively skewed distributions, Gamma, Weibull, and Log-normal, were evaluated. Descriptive analysis indicated that the dataset was predominantly right-skewed (Skp > 0). The GOF results show that the Weibull distribution provides the best representation of the wind speed data (p=0.02), followed by the distributions of Log-normal and Gamma. The Weibull parameter (α > 1) further confirmed its suitability for the data. The findings suggest that Ikeja may experience higher wind speeds in the future, emphasizing the need for precautionary measures to mitigate potential damage to infrastructure and property. This study provides valuable insights for meteorologists and urban planners in anticipating and managing climate-related risks in the city.

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