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

Thao Nguyen-Da, Yi-Min Li, Chi-Lu Peng (), Ming-Yuan Cho and Phuong Nguyen-Thanh
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Thao Nguyen-Da: Department of Tourism Management, Business Intelligence School, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Yi-Min Li: Department of Tourism Management, Business Intelligence School, National Kaohsiung University of Science and Technology, Kaohsiung 807618, 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

Sustainability, 2023, vol. 15, issue 9, 1-22

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.

Keywords: tourism prediction; COVID-19 impact; impact of international and domestic holidays; convolution neural network; hyperparameter fine-tuning; long short-term memory; sustainable tourism (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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