Yıl: 2024 Cilt: 12 Sayı: 1 Sayfa Aralığı: 703 - 717 Metin Dili: İngilizce DOI: 10.21325/jotags.2024.1402 İndeks Tarihi: 24-04-2024

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

Öz:
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.
Anahtar Kelime: Artificial intelligence Machine learning Levenberg-Marquardt Optimization K* algorithm

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Agarwal, P., Swami, S., & Malhotra, S. (2022). Artificial Intelligence Adoption in the Post COVID-19 New-Normal and Role of Smart Technologies in Transforming Business: a Review. Journal of Science and Technology Policy Management. DOI: http://dx.doi.org/10.1108/JSTPM-08-2021-0122.
  • Ahmed, A., Yafouz, A., Birima, A., Kisi, O., Huang, Y., Mohsen S., Sefelnasr, A., & El-Shafie, A. (2022). Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia. Engineering Applications of Computational Fluid Mechanics, 16 (1), 422-440. DOI: 10.1080/19942060.2021.2019128.
  • Arabadzhyan, A., Figini, P., & Zirulia, L. (2021). Hotels, prices and risk premium in exceptional times: The case of Milan hotels during the first COVID-19 outbreak. Annals of Tourism Research Empirical Insights, 2, 100023. DOI: https://doi.org/10.1016/j.annale.2021.100023.
  • Bi, J.-W., Han, T.-Y., & Li, H. (2022). International tourism demand forecasting with machine learning models: The power of the number of lagged inputs. Tourism Economics, 28 (3), 621–645. DOI: https://doi.org/10.1177/1354816620976954.
  • Brune, S., Knollenberg, W., & Vila, O. (2023). Agritourism resilience during the COVID-19 crisis. Annals of Tourism Research, 99, 1035238. DOI: https://doi.org/10.1016/j.annals.2023.103538.
  • Buckley, R., & Westaway, D. (2020). Mental health rescue effects of women's outdoor tourism: A role in COVID-19 recovery. Annals of Tourism Research, 85, 103041. DOI: https://doi.org/10.1016/j.annals.2020.103041.
  • Chang, Y.W., & Liao, M.Y. (2010) A Seasonal ARIMA Model of Tourism Forecasting: The Case of Taiwan. Asia Pacific Journal of Tourism Research, 15:2, 215-221, DOI: 10.1080/10941661003630001.
  • Chen, M., Demir, E., Gomez, C., & Zaremba, A. (2020). The impact of policy responses to COVID-19 on U.S. travel and leisure companies. Annals of Tourism Research Empirical Insights, 1, 100003. DOI: http://dx.doi.org/10.1016/j.annale.2020.100003.
  • Chen, S., Wang, X., Zhang, H., Wang, J., & Peng, J. (2021). Customer purchase forecasting for online tourism: A data-driven method with multiplex behavior data. Tourism Management, Volume 87, 104357, ISSN 0261-5177. DOI: https://doi.org/10.1016/j.tourman.2021.104357.
  • Chen, X., Wu, H., Lichti, D., Han, X., Ban, Y. Li, P., & Deng, H. (2022). Extraction of indoor objects based on the exponential function density clustering model. Information Sciences, Volume 607, Pages 1111-1135, ISSN 0020-0255. DOI: https://doi.org/10.1016/j.ins.2022.06.032.
  • Cho, V. (2001). Tourism Forecasting and its Relationship with Leading Economic Indicators. Journal of Hospitality & Tourism Research, 25(4), 399–420. DOI: https://doi.org/10.1177/109634800102500404.
  • Cleary, J., & L. Trigg (1995). K*: An Instance-based Learner Using an Entropic Distance Measure, in 12th International Conference on Machine Learning. p. 108-114.
  • Fletcher, J., & Morakabati, Y. (2008). Tourism activity, terrorism and political instability within the commonwealth: the cases of Fiji and Kenya. International Journal of Tourism Research, 10 (6), 537-556. DOI: https://doi.org/10.1002/jtr.699.
  • Fu, Y. Downey, A., Yuan, L., Zhang, T., Pratt, A., & Balogun, Y. (2022). Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review. Journal of Manufacturing Processes, Volume 75, 2022, Pages 693-710, ISSN 1526-6125. DOI: https://doi.org/10.1016/j.jmapro.2021.12.061.
  • Heidari, A., Navimipour, N., Unal, M., & Toumaj, S. (2022). Machine learning applications for COVID-19 outbreak management. Neural Computing & Applications, 34, 15313–15348. DOI: https://doi.org/10.1007/s00521-022-07424-w.
  • Hunter, W. (2022). Semiotic fieldwork on chaordic tourism destination image management in Seoul during COVID-19. Tourism Management, 93, 104565. DOI: https://doi.org/10.1016/j.tourman.2022.104565.
  • Hüsser, A.P., & Ohnmacht, T., A. (2023). A comparative study of eight COVID-19 protective measures and their impact on swiss tourists’ travel intentions. Tourism Management, 97, 104734. DOI: https://doi.org/10.1016/j.tourman.2023.104734.
  • Joanne Y., & Roman E. (2021). Color and engagement in touristic Instagram pictures: A machine learning approach. Annals of Tourism Research, Volume 89, 103204, ISSN 0160-7383. DOI: https://doi.org/10.1016/j.annals.2021.103204.
  • Li, X., Li, H., Pan, B., & Law, R. (2021). Machine Learning in Internet Search Query Selection for Tourism Forecasting. Journal of Travel Research, 60 (6), 1213–1231. DOI: https://doi.org/10.1177/0047287520934871.
  • Mach, L., & Ponting, J. (2021). Establishing a pre-COVID-19 baseline for surf tourism: Trip expenditure and attitudes, behaviors and willingness to pay for sustainability. Annals of Tourism Research Empirical Insights, 2, 100011. DOI: http://dx.doi.org/10.1016/j.annale.2021.100011.
  • Milone, F., Gunter, U., & Zekan, B. (2023). The pricing of European airbnb listings during the pandemic: A difference-in-differences approach employing COVID-19 response strategies as a continuous treatment. Tourism Management, 97, 104738. DOI: https://doi.org/10.1016/j.tourman.2023.104738.
  • Morakabati, Y. (2020). A question of confidence. Is tourism as vulnerable to civil unrest as we think? A comparative analysis of the impact of Arab Spring on total reserves and tourism receipts. International Journal of Tourism Research, 22 (2), 252-265. DOI: https://doi.org/10.1002/jtr.2333.
  • Müller, A., & Wittmer, A. (2023). The choice between business travel and video conferencing after COVID-19 – Insights from a choice experiment among frequent travelers. Tourism Management, 96, 104688. DOI: https://doi.org/10.1016/j.tourman.2022.104688.
  • Oh, M. & Kim, S. (2022). Role of Emotions in Fine Dining Restaurant Online Reviews: The Applications of Semantic Network Analysis and a Machine Learning Algorithm. International Journal of Hospitality & Tourism Administration, 23 (5), 875-903. DOI: 10.1080/15256480.2021.1881938.
  • Qiu, R., Park, J., Li, S., & Song, H. (2020). Social costs of tourism during the COVID-19 pandemic. Annals of Tourism Research, 84, 102994. DOI: https://doi.org/10.1016/j.annals.2020.102994.
  • Palmer, A., Montano, J.J., & Sese, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27 (5): 781-790. DOI: https://doi.org/10.1016/j.tourman.2005.05.006.
  • Park, I., Kim, J., Kim, S., Lee, J., & Giroux, M. (2021). Impact of the COVID-19 pandemic on travelers’ preference for crowded versus non-crowded options. Tourism Management, 87, 104398. DOI: https://doi.org/10.1016/j.tourman.2021.104398.
  • Sangkaew, N. & Zhu, H. (2022). Understanding Tourists’ Experiences at Local Markets in Phuket: An Analysis of TripAdvisor Reviews. Journal of Quality Assurance in Hospitality & Tourism, 23 (1), 89-114. DOI: 10.1080/1528008X.2020.1848747.
  • Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, 312-321. DOI: https://doi.org/10.1016/j.jbusres.2020.06.015.
  • Sutthimat, P., Rujivan, S., Mekchay, K., & Rakwongwan, U. (2022). Analytical formula for conditional expectations of path-dependent product of polynomial and exponential functions of extended Cox–Ingersoll–Ross process. Research in the Mathematical Sciences, 9 (10). DOI: https://doi.org/10.1007/s40687-021-00309-9.
  • Turkish Statistical Institute. Statistical Tables, Tourism Income and number of visitors. Available online: https://data.tuik.gov.tr (accessed on 30 December 2022).
  • Ünlüönen K, & Kiliçlar A. (2004). Eighty Years of Turkish Tourism with Economical Reflections. Journal of Gazi University Faculty of Commerce and Tourism Education, 0(1), 131 - 156.
  • Vargas-Sánchez, A. (2018). Crisis Situations in Tourist Destinations: How Can They Be Managed? Enlightening Tourism. A Pathmaking Journal, 8(1), 47-69. DOI: https://doi.org/10.33776/et.v8i1.3439.
  • Vargas, A. (2020). Covid-19 crisis: a new model of tourism governance for a new time. Worldwide Hospitality and Tourism Themes, 12 (6), 691-699. DOI: https://doi.org/10.1108/WHATT-07-2020-0066.
  • Wang, C., Meng, X., Siriwardana, M., & Pham, T. (2022). The impact of COVID-19 on the Chinese tourism industry. Tourism Economics, 28(1), 131–152. DOI: https://doi.org/10.1177/13548166211041209.
  • William L., Park, S., Pan, B., & Newman, P. (2019). Forecasting campground demand in US national parks. Annals of Tourism Research, Volume 75, Pages 424-438, ISSN 0160-7383. DOI: https://doi.org/10.1016/j.annals.2019.01.013.
  • World Health Organization. (2020). WHO Director-General's opening remarks at the media briefing on COVID-19-11 March 2020.
  • World Health Organization. (2020). Global Surveillance for Human Infection with Novel Coronavirus (2019-Ncov): Interim Guidance, 21 January 2020 (No. WHO/2019-nCoV/Surveillance Guidance/2020.1). World Health Organization.
  • World Tourism Organization. (2021). Tourism Highlights 2021 Edition. Retrieved from https://www.unwto.org/tourism-highlights-2021-edition.
  • Wyatt, M., Radford, B., Callow, N., Bennamoun, M. & Hickey, S. (2022). Using ensemble methods to improve the robustness of deep learning for image classification in marine environments. Methods in Ecology and Evolution, 13, 1317– 1328. DOI: https://doi.org/10.1111/2041-210X.13841.
  • Yang, E., & Smith, J. (2023). The spatial and temporal resilience of the tourism and outdoor recreation industries in the United States throughout the COVID-19 pandemic. Tourism Management, 95, 104661. DOI: https://doi.org/10.1016/j.tourman.2022.104661.
  • Yang, H., Wang, L., Xu, Y., & Liu, X. (2023). CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images. International Journal of Machine Learning and Cybernetics. DOI: https://doi.org/10.1007/s13042-022-01676-7.
  • Yang, Y., Tian, X., Ng, W., Wang, R., & Kwong, S. (2022).Generative face inpainting hashing for occluded face retrieval. International Journal of Machine Learning and Cybernetics. DOI: https://doi.org/10.1007/s13042-022-01723-3.
  • Yiwei, W., Najaf, K., Frederico, G., & Atayah, O. (2022). Influence of COVID-19 pandemic on the tourism sector: evidence from China and United States stocks. Current Issues in Tourism, 23 (23), 3783-3798. DOI: https://doi.org/10.1080/13683500.2021.1972944.
  • Yousaf, S. (2021). Travel burnout: Exploring the return journeys of pilgrim-tourists amidst the COVID-19 pandemic. Tourism Management, 84, 104285. DOI: https://doi.org/10.1016/j.tourman.2021.104285.
  • Yu, L., Zhao P., Tang, J., & Pang, L. (2023). Changes in tourist mobility after COVID-19 outbreaks. Annals of Tourism Research, 98, 103522. DOI: https://doi.org/10.1016/j.annals.2022.103522.
  • Zhu N., Zhang D., Wang W., Li X., Yang B., Song J., & Tan, W. (2020). A novel coronavirus from patients with pneumonia in China, 2019. New England Journal of Medicine. DOI: 10.1056/NEJMoa2001017.
APA ŞEKER F, 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. , 703 - 717. 10.21325/jotags.2024.1402
Chicago ŞEKER FERHAT,BOZKURT ALPER 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. (2024): 703 - 717. 10.21325/jotags.2024.1402
MLA ŞEKER FERHAT,BOZKURT ALPER 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. , 2024, ss.703 - 717. 10.21325/jotags.2024.1402
AMA ŞEKER F,BOZKURT A 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. . 2024; 703 - 717. 10.21325/jotags.2024.1402
Vancouver ŞEKER F,BOZKURT A 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. . 2024; 703 - 717. 10.21325/jotags.2024.1402
IEEE ŞEKER F,BOZKURT A "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." , ss.703 - 717, 2024. 10.21325/jotags.2024.1402
ISNAD ŞEKER, FERHAT - BOZKURT, ALPER. "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". (2024), 703-717. https://doi.org/10.21325/jotags.2024.1402
APA ŞEKER F, 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 and Gastronomy Studies, 12(1), 703 - 717. 10.21325/jotags.2024.1402
Chicago ŞEKER FERHAT,BOZKURT ALPER 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 and Gastronomy Studies 12, no.1 (2024): 703 - 717. 10.21325/jotags.2024.1402
MLA ŞEKER FERHAT,BOZKURT ALPER 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 and Gastronomy Studies, vol.12, no.1, 2024, ss.703 - 717. 10.21325/jotags.2024.1402
AMA ŞEKER F,BOZKURT A 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 and Gastronomy Studies. 2024; 12(1): 703 - 717. 10.21325/jotags.2024.1402
Vancouver ŞEKER F,BOZKURT A 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 and Gastronomy Studies. 2024; 12(1): 703 - 717. 10.21325/jotags.2024.1402
IEEE ŞEKER F,BOZKURT A "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 and Gastronomy Studies, 12, ss.703 - 717, 2024. 10.21325/jotags.2024.1402
ISNAD ŞEKER, FERHAT - BOZKURT, ALPER. "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 and Gastronomy Studies 12/1 (2024), 703-717. https://doi.org/10.21325/jotags.2024.1402