Shaolong Sun
Biographic Data
| ID | 6304634 |
|---|---|
| NAME | Shaolong Sun |
| GIVEN NAMES | Shaolong |
| FAMILY NAME | Sun |
| SIGNATURE | SUN S |
| AFFILIATIONS | Xi'an Jiaotong University |
| ORCID | 0000-0002-3196-1459 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 33 |
| WORKS AS AUTHOR | 15 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Spatiotemporal Non‐Stationarity and Driving Mechanisms of Ecological Risk in the Three Gorges Reservoir Area
To address the gaps in existing research regarding multiscale coupling mechanisms of ecological risk and integrated analysis across vertical–horizontal dimensions in mountainous reservoir catchments, this study focuses on the Three Gorges Reservoir Area (TGRA) in China and aims to investigate the spatiotemporal patterns of ecological risk and their underlying drivers through an integrated framework that combines land use analysis with risk assess…
Beyond Visual Appeal
Images, with their direct content experience, have become essential in tourists’ narratives of accommodation sharing. Although visual content plays an important role in influencing user decisions, research on hotel images often neglects the significance of sensory elements. Sensory elements in visual content help shape tourists’ perceptions and evaluations of their accommodation experiences. This study explores how sensory elements in marketing a…
Paying attention to distortion
Accurate multistep-ahead carbon price forecasting is crucial for ensuring the smooth operation of the carbon market, as it provides policy-makers with invaluable insights into future price trends. However, current state-of-the-art carbon price forecasting models, which are trained primarily via the mean squared error loss function, struggle to deliver precise and timely forecasts. To bridge this gap, this study introduces a hybrid multistep-ahead…
Unlocking the power of multimodal online reviews
Daily tourism demand forecasting before and during Covid-19
The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modelling methods can work reliably under the new normal when travel restrictions are eased or lifted. To …
How do tourists perceive churches as tourism destinations? A text mining approach through online reviews
Church tourism has gained popularity due to the historical, artistic, and religious significance of churches, contributing significantly to the social and economic sectors. The destination image of churches plays a crucial role in tourism marketing. However, existing literature predominantly relies on traditional methods to study tourists' perception of churches as tourism destinations, with limited analysis of user‐generated content (UGC) like o…
Daily tourism demand forecasting
Daily tourism demand forecasting can provide important implications for the tourism industry. However, there exist limitations in applying traditional methods to forecast daily tourism demand because of the complex seasonal patterns and holiday effects. In this study, we introduce FB Prophet and apply it to the forecasting of daily tourism demand in the Jiuzhai Valley National Park and Macao from Mainland China. The decomposition result of FB Pro…
Forecasting daily tourism volume
Effective and timely forecasting of daily tourism volume is an important topic for tourism practitioners and researchers, which can reduce waste and promote the sustainable development of tourism. Several studies are based on the decomposition-ensemble model to forecast the time series of high volatility in tourism volume, but ignore different forecasting methods suitable for different subseries. This study provides an adaptive decomposition-ense…
A novel deep learning approach for tourism volume forecasting with tourist search data
Tourism volume forecasting is the hot topic in tourism management, and deep learning techniques as the promising tool are becoming popular for capturing the characteristics of tourism volume data, which is reflected in two aspects: data dimension reduction (i.e., stacked autoencoders [SAE]) and model forecasting (i.e., bi‐directional gated recurrent unit neural network [Bi‐GRU]). With Hong Kong inbound arrivals as a case, this study has empirical…
Regional tourism demand forecasting with spatiotemporal interactions
Regional tourism demand forecasting with spatiotemporal interactions: a multivariate decomposition deep learning model
Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid Covid-19? Mixed-data sampling approach
Tourism demand forecasting
The availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoder and kernel-based extreme learning machine (B-SAKE) is proposed to address these challenges in this study. By using…
Seasonal and trend forecasting of tourist arrivals
In this study, we propose an adaptive multiscale ensemble (AME) learning approach, which consists of variational mode decomposition (VMD) and least square support vector regression (LSSVR) for seasonal and trend forecasting of tourist arrivals. In the formulation of AME learning approach, the original tourist arrival series is decomposed into the trend, seasonal, and remaining volatility components. Then, ARIMA is used to forecast the trend compo…
PM2.5 concentration forecasting through a novel multi-scale ensemble learning approach considering intercity synergy
Forecasting tourist arrivals with machine learning and internet search index
Forecasting tourist arrivals with machine learning and internet search index
Tourism demand forecasting
The availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoder and kernel-based extreme learning machine (B-SAKE) is proposed to address these challenges in this study. By using…
Seasonal and trend forecasting of tourist arrivals
In this study, we propose an adaptive multiscale ensemble (AME) learning approach, which consists of variational mode decomposition (VMD) and least square support vector regression (LSSVR) for seasonal and trend forecasting of tourist arrivals. In the formulation of AME learning approach, the original tourist arrival series is decomposed into the trend, seasonal, and remaining volatility components. Then, ARIMA is used to forecast the trend compo…
PM2.5 concentration forecasting through a novel multi-scale ensemble learning approach considering intercity synergy
Daily tourism demand forecasting
Daily tourism demand forecasting can provide important implications for the tourism industry. However, there exist limitations in applying traditional methods to forecast daily tourism demand because of the complex seasonal patterns and holiday effects. In this study, we introduce FB Prophet and apply it to the forecasting of daily tourism demand in the Jiuzhai Valley National Park and Macao from Mainland China. The decomposition result of FB Pro…
Forecasting daily tourism volume
Effective and timely forecasting of daily tourism volume is an important topic for tourism practitioners and researchers, which can reduce waste and promote the sustainable development of tourism. Several studies are based on the decomposition-ensemble model to forecast the time series of high volatility in tourism volume, but ignore different forecasting methods suitable for different subseries. This study provides an adaptive decomposition-ense…
A novel deep learning approach for tourism volume forecasting with tourist search data
Tourism volume forecasting is the hot topic in tourism management, and deep learning techniques as the promising tool are becoming popular for capturing the characteristics of tourism volume data, which is reflected in two aspects: data dimension reduction (i.e., stacked autoencoders [SAE]) and model forecasting (i.e., bi‐directional gated recurrent unit neural network [Bi‐GRU]). With Hong Kong inbound arrivals as a case, this study has empirical…
Regional tourism demand forecasting with spatiotemporal interactions
Regional tourism demand forecasting with spatiotemporal interactions: a multivariate decomposition deep learning model
Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid Covid-19? Mixed-data sampling approach
Daily tourism demand forecasting before and during Covid-19
The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modelling methods can work reliably under the new normal when travel restrictions are eased or lifted. To …
How do tourists perceive churches as tourism destinations? A text mining approach through online reviews
Church tourism has gained popularity due to the historical, artistic, and religious significance of churches, contributing significantly to the social and economic sectors. The destination image of churches plays a crucial role in tourism marketing. However, existing literature predominantly relies on traditional methods to study tourists' perception of churches as tourism destinations, with limited analysis of user‐generated content (UGC) like o…
Paying attention to distortion
Accurate multistep-ahead carbon price forecasting is crucial for ensuring the smooth operation of the carbon market, as it provides policy-makers with invaluable insights into future price trends. However, current state-of-the-art carbon price forecasting models, which are trained primarily via the mean squared error loss function, struggle to deliver precise and timely forecasts. To bridge this gap, this study introduces a hybrid multistep-ahead…
Unlocking the power of multimodal online reviews
Spatiotemporal Non‐Stationarity and Driving Mechanisms of Ecological Risk in the Three Gorges Reservoir Area
To address the gaps in existing research regarding multiscale coupling mechanisms of ecological risk and integrated analysis across vertical–horizontal dimensions in mountainous reservoir catchments, this study focuses on the Three Gorges Reservoir Area (TGRA) in China and aims to investigate the spatiotemporal patterns of ecological risk and their underlying drivers through an integrated framework that combines land use analysis with risk assess…
Beyond Visual Appeal
Images, with their direct content experience, have become essential in tourists’ narratives of accommodation sharing. Although visual content plays an important role in influencing user decisions, research on hotel images often neglects the significance of sensory elements. Sensory elements in visual content help shape tourists’ perceptions and evaluations of their accommodation experiences. This study explores how sensory elements in marketing a…
Computer Science (11 works) · Geography (11 works) · Tourism (11 works) · Diverse Aspects of Tourism Research (9 works) · Econometrics (8 works) · Artificial Intelligence (7 works) · China (6 works) · Economics (6 works) · Machine learning (6 works) · Data mining (5 works)