What if the Covid-19 Pandemic Never Happened? Estimation of the Tourist Arrivals for 2020 Via Levenberg-Marquardt Optimization and K-Star (K*) Machine Learning Algorithms


  • Ferhat ŞEKER ŞEKER
  • Alper BOZKURT




Artificial intelligence, Machine learning, Levenberg-Marquardt Optimization, K* algorithm


Based on the probability theory, which is used to model uncertainty and randomness in real-world situations, the study aims to understand the impact of uncertain conditions, such as the Covid-19 pandemic, on the accuracy of the algorithms and the resulting losses to a country's tourism industry. The contribution of this paper to the international body of knowledge is twofold: firstly, it advances theoretical understanding of the use of probability theory in modelling real-world problems; and secondly, it offers a methodological approach for estimating tourist arrivals that accounts for the impact of extreme events. To achieve these aims, the Levenberg-Marquardt optimization was first applied to determine the optimal coefficients of the exponential function for estimating tourist arrivals from 1950 to 2020. Next, the K-Star machine learning algorithm was applied to the dataset with and without Covid-19 cases to estimate tourist arrivals.


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How to Cite

ŞEKER , F. ŞEKER, & BOZKURT , A. (2024). What if the Covid-19 Pandemic Never Happened? Estimation of the Tourist Arrivals for 2020 Via Levenberg-Marquardt Optimization and K-Star (K*) Machine Learning Algorithms. Journal of Tourism & Gastronomy Studies, 12(1), 703–717. https://doi.org/10.21325/jotags.2024.1402