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

Nguyen-Da, Thao; Li, Yimin; Peng, Chi-Lu; Cho, Ming-Yuan; Thanh, Phuong Nguyen

doi:10.3390/su15097179

Cited by 10 publications

(1 citation statement)

References 63 publications

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“…While traditional machine learning algorithms often fail to capture intricate relationships between predictors and outcomes, deep learning models, such as neural networks, can effectively learn these complex patterns through multiple layers of hierarchical representations. Consequently, they have proven to be highly effective in a range of applications, from autonomous driving [24] to medical imaging [25] and finance [26]. Overall, the ability of deep learning models to accurately fit complex data makes them a powerful tool in data analysis and decision-making.…”

Section: Introductionmentioning

confidence: 99%

Digital Integration of Temperature Field of Cable-Stayed Bridge Based on Finite Element Model Updating and Health Monitoring

Zhong

Song

et al. 2023

Sustainability

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A health monitoring system typically collects and processes data to observe the health status of a bridge. The cost limitations imply that only the measurement point data of a few key points can be obtained; however, the entire bridge monitoring information cannot be established, which significantly interferes with the data integrity of the structural monitoring system. In this study, a solution is proposed for reconstructing the monitoring data of the entire bridge. By updating the finite element (FE) model based on structural thermal analysis, numerical simulation technology, and other methods, the temperature field integration model of a cable-stayed bridge is realized. The temperature spatial expansion method of deep learning is established by using the complete simulated temperature field of the entire bridge based on limited measured temperature data; this data is verified and applied to the main beam and bridge tower, thereby establishing the complete measured temperature field of the whole bridge. This is of great significance in supplementing health monitoring information, allowing for the accurate monitoring and evaluation of the structural safety and service performance of long bridges.

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Section: Introductionmentioning

confidence: 99%

Digital Integration of Temperature Field of Cable-Stayed Bridge Based on Finite Element Model Updating and Health Monitoring

Zhong

Song

et al. 2023

Sustainability

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Monthly Tourism Demand Forecasting With COVID‐19 Impact‐Based Hybrid Convolution Neural Network and Gate Recurrent Unit

Nguyen,

Li,

Peng

et al. 2024

Journal of Tourism Research

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The accuracy of tourism demand (TD) prediction, essential for managing available resources in the tourism industry, still needs to be improved with the unreliability of traditional algorithms. This research proposes a deep learning methodology that combines the convolution neural network (CNN) and gated recurrent unit (GRU), efficiently predicting Vietnam's tourism demand. The Pearson correlation coefficients are performed to nominate the most appropriate feature inputs. The proposed algorithm is analyzed and evaluated with other benchmark approaches, comprising the recurrent neural network (RNN), the long short‐term memory (LSTM), the GRU, and the CNN. The experiments prove that the developed hybrid algorithm could outperform previous methodologies in predicting TD in some of Vietnam's provinces. The proposed algorithm could provide satisfactory predictions for tourism demand with a supreme enhancement of 77.1% MSE, 37.4% validating MSE, 46.0% MAE, 20.6% validating MAE, 76.6% MAPE, and 90.3% validating MAPE comparing across deep learning benchmarks.

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

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

Cited by 10 publications

References 63 publications

Digital Integration of Temperature Field of Cable-Stayed Bridge Based on Finite Element Model Updating and Health Monitoring

Digital Integration of Temperature Field of Cable-Stayed Bridge Based on Finite Element Model Updating and Health Monitoring

Monthly Tourism Demand Forecasting With COVID‐19 Impact‐Based Hybrid Convolution Neural Network and Gate Recurrent Unit

Machine learning applied to tourism: A systematic review

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