Impact of COVID-19 on latent emissiveness of residents in China: Based on the X-12-ARIMA additive seasonal adjustment model

Hou, Lian-Lian

doi:10.3233/jcm-215473

Cited by 3 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, while the above studies are helpful in understanding the impact of emergencies on the overall tourism industry, businesses, and destinations, they neglect to focus on tourists, whose feelings during an emergency are more real, urgent, and accurate than after it has occurred. Thus, when studying the impact of an outbreak on tourism, observations and analysis should be centered on tourists [22]. It is worth exploring tourists' potential willingness to travel during an outbreak and whether the intensity of this willingness is different compared to normal times.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Seasonality and the Impact of COVID-19 on Tourists’ Use of Urban Green Space in Okinawa: An ARIMA Modeling Approach Using Web Review Data

Yang

Liu

et al. 2023

Land

View full text Add to dashboard Cite

We employed publicly available user-generated content (UGC) data from the website Tripadvisor and developed an autoregressive integrated moving average (ARIMA) model using the R language to analyze the seasonality of the use of urban green space (UGS) in Okinawa under normal conditions and during the COVID-19 pandemic. The seasonality of the use of ocean-area UGS is primarily influenced by climatic factors, with the peak season occurring from April to October and the off-peak season from November to March. Conversely, the seasonality of the use of non-ocean-area UGS remains fairly stable throughout the year, with a relatively high number of visitors in January and May. The outbreak of the COVID-19 pandemic greatly impacted visitor enthusiasm for travel, resulting in significantly fewer actual postings compared with predictions. During the outbreak, use of ocean-area UGS was severely restricted, resulting in even fewer postings and a negative correlation with the number of new cases. In contrast, for non-ocean-area UGS, a positive correlation was observed between the change in postings and the number of new cases. We offer several suggestions to develop UGS management in Okinawa, considering the opportunity for a period of recovery for the tourism industry.

show abstract

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Seasonality and the Impact of COVID-19 on Tourists’ Use of Urban Green Space in Okinawa: An ARIMA Modeling Approach Using Web Review Data

Yang

Liu

et al. 2023

Land

View full text Add to dashboard Cite

show abstract

NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Gao

Du²,

Lei³

et al. 2023

Water Resour Manage

View full text Add to dashboard Cite

Normalized difference vegetation index(NDVI) is the most commonly used factor to re ect vegetation growth status, and improving the prediction accuracy of NDVI is of great signi cance to the development of regional ecology. In this study, a new NDVI forecasting model based on the combination of time series decomposition(TSD), convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In order to verify the performance of TSD-CNN-LSTM model and explore the response of NDVI to climatic factors, two forecasting models of temperature and precipitation based on its own historical information and four NDVI forecasting models that based on Temperature, precipitation and its own historical information were established. The results show that TSD-CNN-LSTM model based on its own historical information had the best forecasting performance, with the RMSE, NSE, MAE to be 0.4996, 0.9981, 0.4169 for temperature, 5.6941, 0.9822, 3.9855 for precipitation and 0.0573, 0.9617, 0.0447 for NDVI, respectively. Meanwhile, the NDVI forecasting models based on climatic factors show that the model based on the combination of temperature and precipitation has the better effect than that based on single temperature and single precipitation. Combined with the results of correlation analysis it can be inferred that NDVI changes are most signi cantly in uenced by the combination of temperature and precipitation, followed by temperature, and least in uenced by precipitation. The above ndings can provide a meaningful reference and guidance for the study of vegetation growth with climate changes.

show abstract

NDVI forecasting model based on the combination of Time series decomposition and CNN - LSTM

Gao

Lei

et al. 2022

Preprint

View full text Add to dashboard Cite

Normalized difference vegetation index(NDVI) is the most commonly used factor to reflect vegetation growth status, and improving the prediction accuracy of NDVI is of great significance to the development of regional ecology. In this study, a new NDVI forecasting model based on the combination of time series decomposition(TSD), convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In order to verify the performance of TSD-CNN-LSTM model and explore the response of NDVI to climatic factors, two forecasting models of temperature and precipitation based on its own historical information and four NDVI forecasting models that based on Temperature, precipitation and its own historical information were established. The results show that TSD-CNN-LSTM model based on its own historical information had the best forecasting performance, with the RMSE, NSE, MAE to be 0.4996, 0.9981, 0.4169 for temperature, 5.6941, 0.9822, 3.9855 for precipitation and 0.0573, 0.9617, 0.0447 for NDVI, respectively. Meanwhile, the NDVI forecasting models based on climatic factors show that the model based on the combination of temperature and precipitation has the better effect than that based on single temperature and single precipitation. Combined with the results of correlation analysis it can be inferred that NDVI changes are most significantly influenced by the combination of temperature and precipitation, followed by temperature, and least influenced by precipitation. The above findings can provide a meaningful reference and guidance for the study of vegetation growth with climate changes.

show abstract

Impact of COVID-19 on latent emissiveness of residents in China: Based on the X-12-ARIMA additive seasonal adjustment model

Cited by 3 publications

References 27 publications

Quantitative Analysis of Seasonality and the Impact of COVID-19 on Tourists’ Use of Urban Green Space in Okinawa: An ARIMA Modeling Approach Using Web Review Data

Quantitative Analysis of Seasonality and the Impact of COVID-19 on Tourists’ Use of Urban Green Space in Okinawa: An ARIMA Modeling Approach Using Web Review Data

NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

NDVI forecasting model based on the combination of Time series decomposition and CNN - LSTM

Contact Info

Product

Resources

About