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Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces

Author

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  • Thao Nguyen-Da

    (Department of Tourism Management, Business Intelligence School, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
    Faculty of Economics and Management, Thai Binh Duong University, Khanh Hoa 650000, Vietnam)

  • Yi-Min Li

    (Department of Tourism Management, Business Intelligence School, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
    The International Master’s Program of Tourism and Hospitality, National Kaohsiung University of Hospitality & Tourism, Kaohsiung 812301, Taiwan)

  • Chi-Lu Peng

    (Department of Public Finance and Taxation, Business Intelligent School, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan)

  • Ming-Yuan Cho

    (Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan)

  • Phuong Nguyen-Thanh

    (Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
    Department of Electronic–Electrical Engineering, Nha Trang University, Khanh Hoa 650000, Vietnam)

Abstract

The tourism industry experienced a positive increase after COVID-19 and is the largest segment in the foreign exchange contribution in developing countries, especially in Vietnam, where China has begun reopening its borders and lifted the pandemic limitation on foreign travel. This research proposes a hybrid algorithm, combined convolution neural network (CNN) and long short-term memory (LSTM), to accurately predict the tourism demand in Vietnam and some provinces. The number of new COVID-19 cases worldwide and in Vietnam is considered a promising feature in predicting algorithms, which is novel in this research. The Pearson matrix, which evaluates the correlation between selected features and target variables, is computed to select the most appropriate input parameters. The architecture of the hybrid CNN–LSTM is optimized by utilizing hyperparameter fine-tuning, which improves the prediction accuracy and efficiency of the proposed algorithm. Moreover, the proposed CNN–LSTM outperformed other traditional approaches, including the backpropagation neural network (BPNN), CNN, recurrent neural network (RNN), gated recurrent unit (GRU), and LSTM algorithms, by deploying the K-fold cross-validation methodology. The developed algorithm could be utilized as the baseline strategy for resource planning, which could efficiently maximize and deeply utilize the available resource in Vietnam.

Suggested Citation

  • Thao Nguyen-Da & Yi-Min Li & Chi-Lu Peng & Ming-Yuan Cho & Phuong Nguyen-Thanh, 2023. "Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7179-:d:1132708
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    References listed on IDEAS

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    1. Gunter, Ulrich & Önder, Irem, 2015. "Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data," Tourism Management, Elsevier, vol. 46(C), pages 123-135.
    2. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    3. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    4. Tea Baldigara & Maja Mamula, 2015. "Modelling international tourism demand using seasonal ARIMA models," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 21(1), pages 19-31, May.
    5. Yoonsuh Jung, 2018. "Multiple predicting K-fold cross-validation for model selection," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 197-215, January.
    6. Fengjun Tian & Yang Yang & Lan Jiang, 2022. "Spatial spillover of transport improvement on tourism growth," Tourism Economics, , vol. 28(5), pages 1416-1432, August.
    7. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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