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Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods

Yıl 2022, Cilt: 30 Sayı: 53, 11 - 26, 29.07.2022

Mehmet Kayakuş

https://doi.org/10.17233/sosyoekonomi.2022.03.01
Cited By: 2

Öz

In this study, the number of visitors of five different tourism agencies was tried to be estimated by machine learning method using the number of cases and deaths in Europe during COVID-19. Artificial neural network (ANN), support vector regression (SVR), and multiple linear regression (MLR) were used as machine learning models. A model consisting of two independent variables and one dependent variable was created. According to the analysis made according to three different techniques, the most successful results; According to R2, it was seen that ANN, DVR, and MDR, and according to other statistical methods, ANN, MDR, and DVR, respectively.

Anahtar Kelimeler

COVID-19 Pandemic Tourism Agency Website Number of Visitors Machine Learning

Kaynakça

  • Aliperti, G. et al. (2019), “Tourism, crisis, disaster: An interdisciplinary approach”, Annals of Tourism Research, 79, 102808, 1-5.
  • Andrew, W.P. et al. (1990), “Forecasting hotel occupancy rates with time series models: An empirical analysis”, Hospitality Research Journal, 14(2), 173-182.
  • Bishop, C.M. (1995), Neural networks for pattern recognition, Oxford University Press.
  • Bloom, J.Z. (2005), “Market segmentation: A neural network application”, Annals of Tourism Research, 32(1), 93-111.
  • Brown, S.H. (2009), “Multiple linear regression analysis: a matrix approach with MATLAB”, Alabama Journal of Mathematics, 34, 1-3.
  • Chen, R. et al. (2015), “Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm”, Applied Soft Computing, 26, 435-443.
  • Cró, S. & A.M. Martins (2017), “Structural breaks in international tourism demand: Are they caused by crises or disasters?” Tourism Management, 63, 3-9.
  • Cunliffe, D. (2000), “Developing usable Web sites-a review and model”, Internet Research, 10(4), 295-308.
  • European Union Agency (2020), <https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-COVID-19-cases-worldwide>, 23.12.2020.
  • Farzanegan, M.R. et al. (2020), “International tourism and outbreak of coronavirus (COVID-19): A cross-country analysis”, Journal of Travel Research, 60(3), 687-692.
  • Gössling, S. et al. (2020), “Pandemics, tourism and global change: a rapid assessment of COVID-19”, Journal of Sustainable Tourism, 29(1), 1-20.
  • Grégoire, G. (2014), “Multiple linear regression”, EAS Publications Series, 66, 45-72.
  • Gunn, S.R. (1998), “Support vector machines for classification and regression”, ISIS technical report, 14(1), 5-16.
  • Hill, T. & W. Remus (1994), “Neural network models for intelligent support of managerial decision making”, Decision Support Systems, 11(5), 449-459.
  • Kasavana, M.L. et al. (1998), “Netlurking: The future of hospitality Internet marketing”, Journal of Hospitality & Leisure Marketing, 5(1), 31-44.
  • Kayakuş, M. (2021), “Determination of Ideal Artificial Neural Networks Parameters for Software Project Effort Estimation”, European Journal of Science and Technology, 22, 43-48.
  • Kim, J. et al. (2003), “Segmenting the market of West Australian senior tourists using an artificial neural network”, Tourism Management, 24(1), 25-34.
  • Klimasauskas, C.C. (1992), “Neural networks: An engineering perspective”, IEEE Communications Magazine, 30(9), 50-53.
  • Kotler, P. et al. (2017), Marketing for hospitality and tourism, Pearson Education India.
  • Law, R. (2000), “Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting”, Tourism Management, 21(4), 331-340.
  • Law, R. & N. Au (1999), “A neural network model to forecast Japanese demand for travel to Hong Kong”, Tourism Management, 20(1), 89-97.
  • Law, R. et al. (2019), Tourism demand forecasting: A deep learning approach”, Annals of Tourism Research, 75, 410-423.
  • Lewis, C.D. (1982), Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, Butterworth-Heinemann.
  • Lijuan, W. & C. Guohua (2016), “Seasonal SVR with FOA algorithm for single-step and multi-step ahead forecasting in monthly inbound tourist flow”, Knowledge-Based Systems, 110, 157-166.
  • Lippmann, R.P. (1988), “An introduction to computing with neural nets”, Acm Sigarch Computer Architecture News, 16(1), 7-25.
  • Mata, J. (2011), “Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models”, Engineering structures, 33(3), 903-910.
  • Murat, Ã. et al. (2014), “Modelling and forecasting cruise tourism demand to Izmir by different artificial neural network architectures”, International Journal of Business and Social Research, 4(3), 12-28.
  • O'Connor, P. (1999), Electronic information distribution in tourism and hospitality, CAB International.
  • Palmer, A. et al. (2006), “Designing an artificial neural network for forecasting tourism time series”, Tourism Management, 27(5), 781-790.
  • Pattie, D.C. & J. Snyder (1996), “Using a neural network to forecast visitor behavior”, Annals of Tourism Research, 23(1), 151-164.
  • Polyzos, S. et al. (2020), “Tourism demand and the COVID-19 pandemic: An LSTM approach”, Tourism Recreation Research, 46(2), 175-187.
  • Pulliam, P. (1999), “To Web or Not to Web? Is not the question, but rather: when and how to Web?”, Direct Marketing, 62(1), 19-24.
  • Richter, L.K. (2003), “International tourism and its global public health consequences”, Journal of travel research, 41(4), 340-347.
  • Rosenbloom, B. et al. (1999), Marketing Channels: A Management View, 6th. Edition, The Dryden.
  • Sio-Chong, U. & Y.C. So (2020), “The impacts of financial and non-financial crises on tourism: Evidence from Macao and Hong Kong”, Tourism Management Perspectives, 33, 175-187.
  • Smola, A.J. & B. Schölkopf (1998), “On a kernel-based method for pattern recognition, regression, approximation, and operator inversion”, Algorithmica, 22(1-2), 211-231.
  • So, S.-I.A. & A.M. Morrison (2004), “Internet marketing in tourism in Asia: an evaluation of the performance of East Asian national tourism organisation websites”, Journal of Hospitality & Leisure Marketing, 11(4), 93-118.
  • Temel, G.O. et al. (2012), “Usage of Resampling Methods for Evaluating the Performance of Classification Model”, International Journal of Informatics Technologies, 5(3), 1-8.
  • Tsaur, S.-H. et al. (2002), “Determinants of guest loyalty to international tourist hotels-a neural network approach”, Tourism Management, 23(4), 397-405.
  • Vapnik, V. (2013), “The nature of statistical learning theory”, Springer Science & Business Media.
  • Walle, A. (1996), “Tourism and the Internet: opportunities for direct marketing”, Journal of Travel Research, 35(1), 72-77.
  • Wan, C.-S. (2002), “The web sites of international tourist hotels and tour wholesalers in Taiwan”, Tourism Management, 23(2), 155-160.
  • Wang, Y.-S. (2009), “The impact of crisis events and macroeconomic activity on Taiwan's international inbound tourism demand”, Tourism Management, 30(1), 75-82.
  • websiteiq.com (2020), <https://www.websiteiq.com/>, 23.12.2020.
  • World Health Organization (2020), “Coronavirus disease 2019 (COVID-19)”, Situation Report.
  • Witt, S.F. & C.A. Witt (1992), Modelling and forecasting demand in tourism, Academic Press Ltd.

COVID-19 Sürecinde Turizm Acentelerinin Web Sitelerindeki Ziyaretçi Sayısındaki Değişimin Makine Öğrenmesi Yöntemleriyle Tahmin Edilmesi

Yıl 2022, Cilt: 30 Sayı: 53, 11 - 26, 29.07.2022

Mehmet Kayakuş

https://doi.org/10.17233/sosyoekonomi.2022.03.01
Cited By: 2

Öz

Bu çalışmada COVID-19 süresince Avrupa’daki vaka ve ölüm sayıları bilgileri kullanılarak beş farklı turizm acentesinin ziyaretçi sayısı makine öğrenmesi yöntemiyle tahmin edilmeye çalışılmıştır. Yöntem olarak makine öğrenmesi modellerinden yapay sinir ağları (YSA), destek vektör regresyonu (DVR) ve çoklu doğrusal regresyon (ÇDR) kullanılmıştır. İki bağımsız değişken ve bir bağımlı değişkenden oluşan model oluşturulmuştur. Üç farklı tekniğine göre yapılan analize göre en başarılı sonuçların; R2’ye göre YSA, DVR ve ÇDR, diğer istatiksel yöntemlere göre de sırasıyla YSA, ÇDR ve DVR olduğu görülmüştür.

Anahtar Kelimeler

COVID-19 Salgını Turizm Acentesi Web Sitesi Ziyaretçi Sayısı Makine Öğrenmesi

Kaynakça

  • Aliperti, G. et al. (2019), “Tourism, crisis, disaster: An interdisciplinary approach”, Annals of Tourism Research, 79, 102808, 1-5.
  • Andrew, W.P. et al. (1990), “Forecasting hotel occupancy rates with time series models: An empirical analysis”, Hospitality Research Journal, 14(2), 173-182.
  • Bishop, C.M. (1995), Neural networks for pattern recognition, Oxford University Press.
  • Bloom, J.Z. (2005), “Market segmentation: A neural network application”, Annals of Tourism Research, 32(1), 93-111.
  • Brown, S.H. (2009), “Multiple linear regression analysis: a matrix approach with MATLAB”, Alabama Journal of Mathematics, 34, 1-3.
  • Chen, R. et al. (2015), “Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm”, Applied Soft Computing, 26, 435-443.
  • Cró, S. & A.M. Martins (2017), “Structural breaks in international tourism demand: Are they caused by crises or disasters?” Tourism Management, 63, 3-9.
  • Cunliffe, D. (2000), “Developing usable Web sites-a review and model”, Internet Research, 10(4), 295-308.
  • European Union Agency (2020), <https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-COVID-19-cases-worldwide>, 23.12.2020.
  • Farzanegan, M.R. et al. (2020), “International tourism and outbreak of coronavirus (COVID-19): A cross-country analysis”, Journal of Travel Research, 60(3), 687-692.
  • Gössling, S. et al. (2020), “Pandemics, tourism and global change: a rapid assessment of COVID-19”, Journal of Sustainable Tourism, 29(1), 1-20.
  • Grégoire, G. (2014), “Multiple linear regression”, EAS Publications Series, 66, 45-72.
  • Gunn, S.R. (1998), “Support vector machines for classification and regression”, ISIS technical report, 14(1), 5-16.
  • Hill, T. & W. Remus (1994), “Neural network models for intelligent support of managerial decision making”, Decision Support Systems, 11(5), 449-459.
  • Kasavana, M.L. et al. (1998), “Netlurking: The future of hospitality Internet marketing”, Journal of Hospitality & Leisure Marketing, 5(1), 31-44.
  • Kayakuş, M. (2021), “Determination of Ideal Artificial Neural Networks Parameters for Software Project Effort Estimation”, European Journal of Science and Technology, 22, 43-48.
  • Kim, J. et al. (2003), “Segmenting the market of West Australian senior tourists using an artificial neural network”, Tourism Management, 24(1), 25-34.
  • Klimasauskas, C.C. (1992), “Neural networks: An engineering perspective”, IEEE Communications Magazine, 30(9), 50-53.
  • Kotler, P. et al. (2017), Marketing for hospitality and tourism, Pearson Education India.
  • Law, R. (2000), “Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting”, Tourism Management, 21(4), 331-340.
  • Law, R. & N. Au (1999), “A neural network model to forecast Japanese demand for travel to Hong Kong”, Tourism Management, 20(1), 89-97.
  • Law, R. et al. (2019), Tourism demand forecasting: A deep learning approach”, Annals of Tourism Research, 75, 410-423.
  • Lewis, C.D. (1982), Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, Butterworth-Heinemann.
  • Lijuan, W. & C. Guohua (2016), “Seasonal SVR with FOA algorithm for single-step and multi-step ahead forecasting in monthly inbound tourist flow”, Knowledge-Based Systems, 110, 157-166.
  • Lippmann, R.P. (1988), “An introduction to computing with neural nets”, Acm Sigarch Computer Architecture News, 16(1), 7-25.
  • Mata, J. (2011), “Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models”, Engineering structures, 33(3), 903-910.
  • Murat, Ã. et al. (2014), “Modelling and forecasting cruise tourism demand to Izmir by different artificial neural network architectures”, International Journal of Business and Social Research, 4(3), 12-28.
  • O'Connor, P. (1999), Electronic information distribution in tourism and hospitality, CAB International.
  • Palmer, A. et al. (2006), “Designing an artificial neural network for forecasting tourism time series”, Tourism Management, 27(5), 781-790.
  • Pattie, D.C. & J. Snyder (1996), “Using a neural network to forecast visitor behavior”, Annals of Tourism Research, 23(1), 151-164.
  • Polyzos, S. et al. (2020), “Tourism demand and the COVID-19 pandemic: An LSTM approach”, Tourism Recreation Research, 46(2), 175-187.
  • Pulliam, P. (1999), “To Web or Not to Web? Is not the question, but rather: when and how to Web?”, Direct Marketing, 62(1), 19-24.
  • Richter, L.K. (2003), “International tourism and its global public health consequences”, Journal of travel research, 41(4), 340-347.
  • Rosenbloom, B. et al. (1999), Marketing Channels: A Management View, 6th. Edition, The Dryden.
  • Sio-Chong, U. & Y.C. So (2020), “The impacts of financial and non-financial crises on tourism: Evidence from Macao and Hong Kong”, Tourism Management Perspectives, 33, 175-187.
  • Smola, A.J. & B. Schölkopf (1998), “On a kernel-based method for pattern recognition, regression, approximation, and operator inversion”, Algorithmica, 22(1-2), 211-231.
  • So, S.-I.A. & A.M. Morrison (2004), “Internet marketing in tourism in Asia: an evaluation of the performance of East Asian national tourism organisation websites”, Journal of Hospitality & Leisure Marketing, 11(4), 93-118.
  • Temel, G.O. et al. (2012), “Usage of Resampling Methods for Evaluating the Performance of Classification Model”, International Journal of Informatics Technologies, 5(3), 1-8.
  • Tsaur, S.-H. et al. (2002), “Determinants of guest loyalty to international tourist hotels-a neural network approach”, Tourism Management, 23(4), 397-405.
  • Vapnik, V. (2013), “The nature of statistical learning theory”, Springer Science & Business Media.
  • Walle, A. (1996), “Tourism and the Internet: opportunities for direct marketing”, Journal of Travel Research, 35(1), 72-77.
  • Wan, C.-S. (2002), “The web sites of international tourist hotels and tour wholesalers in Taiwan”, Tourism Management, 23(2), 155-160.
  • Wang, Y.-S. (2009), “The impact of crisis events and macroeconomic activity on Taiwan's international inbound tourism demand”, Tourism Management, 30(1), 75-82.
  • websiteiq.com (2020), <https://www.websiteiq.com/>, 23.12.2020.
  • World Health Organization (2020), “Coronavirus disease 2019 (COVID-19)”, Situation Report.
  • Witt, S.F. & C.A. Witt (1992), Modelling and forecasting demand in tourism, Academic Press Ltd.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm Makaleler
Yazarlar

Mehmet Kayakuş 0000-0003-0394-5862

Yayımlanma Tarihi 29 Temmuz 2022
Gönderilme Tarihi 14 Şubat 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 30 Sayı: 53

Kaynak Göster

APA Kayakuş, M. (2022). Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods. Sosyoekonomi, 30(53), 11-26. https://doi.org/10.17233/sosyoekonomi.2022.03.01
AMA Kayakuş M. Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods. Sosyoekonomi. Temmuz 2022;30(53):11-26. doi:10.17233/sosyoekonomi.2022.03.01
Chicago Kayakuş, Mehmet. “Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods”. Sosyoekonomi 30, sy. 53 (Temmuz 2022): 11-26. https://doi.org/10.17233/sosyoekonomi.2022.03.01.
EndNote Kayakuş M (01 Temmuz 2022) Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods. Sosyoekonomi 30 53 11–26.
IEEE M. Kayakuş, “Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods”, Sosyoekonomi, c. 30, sy. 53, ss. 11–26, 2022, doi: 10.17233/sosyoekonomi.2022.03.01.
ISNAD Kayakuş, Mehmet. “Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods”. Sosyoekonomi 30/53 (Temmuz 2022), 11-26. https://doi.org/10.17233/sosyoekonomi.2022.03.01.
JAMA Kayakuş M. Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods. Sosyoekonomi. 2022;30:11–26.
MLA Kayakuş, Mehmet. “Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods”. Sosyoekonomi, c. 30, sy. 53, 2022, ss. 11-26, doi:10.17233/sosyoekonomi.2022.03.01.
Vancouver Kayakuş M. Estimating the Changes in the Number of Visitors on the Websites of the Tourism Agencies in the COVID-19 Process by Machine Learning Methods. Sosyoekonomi. 2022;30(53):11-26.

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