Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces
Sustainability
; 15(9):7179, 2023.
Article
Dans Anglais
| ProQuest Central | ID: covidwho-2317677
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.
Environmental Studies; tourism prediction; COVID-19 impact; impact of international and domestic holidays; convolution neural network; hyperparameter fine-tuning; long short-term memory; sustainable tourism; Pandemics; Accuracy; Deep learning; Forecasting; Algorithms; Artificial neural networks; Back propagation networks; Developing countries--LDCs; COVID-19; Machine learning; Time series; Strategic planning; Tourism; Festivals; Short term memory; Neural networks; Medical research; Recurrent neural networks; Gross Domestic Product--GDP; Methods; Coronaviruses; Southeast Asia; Vietnam; Indonesia
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
ProQuest Central
Type d'étude:
Rapport de cas
/
Étude pronostique
langue:
Anglais
Revue:
Sustainability
Année:
2023
Type de document:
Article
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