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August & September  2019, 12(4&5): 837-847. doi: 10.3934/dcdss.2019056

A mathematical analysis for the forecast research on tourism carrying capacity to promote the effective and sustainable development of tourism

Business School, Sichuan University, Chengdu, Sichuan 610064, China

* Corresponding author: Xiaowen Jie, jiexw@vip.163.com

Received  September 2017 Revised  December 2017 Published  November 2018

With the continuous and quick development of Chinese tourism industry over years, ecological environmental problems emerge consequently. The contradiction between the development of tourism economy and the protection of ecological environment has become the focus of scientific experts and Chinese government, and accordingly it is of vital importance to predict tourism carrying capacity accurately. In this paper, a new forecast approach is proposed for government staff and scenic spot management staff on tourist carrying capacity, which promotes the effective, healthy and sustainable development of the tourism country.

Citation: Yanan Wang, Tao Xie, Xiaowen Jie. A mathematical analysis for the forecast research on tourism carrying capacity to promote the effective and sustainable development of tourism. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 837-847. doi: 10.3934/dcdss.2019056
References:
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G. E. P. Box and G. M. Jenkins, Time series analysis, forecasting and control, San Francisco: Holden Day, 1970. Google Scholar

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A. CarreñoA. Vidal-FerrándizD. Ginestar and G. Verdú, Multilevel method to compute the lambda modes of the neutron diffusion equation, Applied Mathematics and Nonlinear Sciences, 2 (2017), 225-236. Google Scholar

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F. ChanC. Lim and M. McAleer, Modelling multivariate international tourism demand and volatility, Tourism Management, 26 (2005), 459-471. Google Scholar

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V. Cho, Tourism forecasting and its relationship with leading economic indicators, Journal of Hospitality and Tourism Research, 25 (2001), 399-420. Google Scholar

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Y. Deng, etal, The processing of boundary problem in EMD method and Hilbert transform, Chinese Science Bulletin, 46 (2001), 257-263.Google Scholar

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J. Gao, Principle of artificial neural network and simulation example, Beijing: China Machine Press, 2003, 1-2.Google Scholar

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C. Goh and R. Law, Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention, Tourism Management, 23 (2002), 499-510. Google Scholar

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W. Guo and J. Li, Tourism demand prediction based on improved optimized combined method, Statistics and Decision, 8 (2011), 75-77. Google Scholar

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X. Guo and L. Wang, New algorithm and application of empirical mode decomposition, Noise and Vibration Prediction, 5 (2008), 70-71. Google Scholar

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N. Huang, etal, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non - stationary time series analysis, Proceedings of the Royal Society of London, 454 (1998), 903-995. doi: 10.1098/rspa.1998.0193. Google Scholar

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N. Kulendran and K. Wilson, Modelling business travel, Tourism Economics, 6 (2000), 47-59. Google Scholar

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N. Kulendran and S. F. & Witt, Leading indicator tourism forecasts, Tourism Management, 24 (2003b), 503-510.Google Scholar

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R. Law, A neural network model to forecast Japanese demand for travel to Hong Kong, Tourism Management, 20 (1999), 89-97. Google Scholar

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K. Lei and Y. Chen, Chinese inbound tourist capacity prediction based on bp neural network and arima combined model, Tourism Tribune, 22 (2007), 20-25. Google Scholar

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K. Lei and Y. Chen, Prediction of chinese inbound tourist capacity based on bp neural network and ARIMA combined model, Tourism Tribune, 22 (2007), 20-25. Google Scholar

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G. LiK. F. WongH. Song and S. F. Witt, Tourism demand forecasting: A time varying parameter error correction model, Journal of Travel Research, 45 (2006), 175-185. Google Scholar

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C. Lim and M. McAleer, Cointegration analysis of quarterly tourism demand by Hong Kong and Singapore for Australia, Applied Economics, 33 (2001a), 1599-1619. Google Scholar

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X. Liu, Improvement and Application of BP Algorithm, Taiyuan: Taiyuan University of Technology, 2012.Google Scholar

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H. Liu, etal, Empirical mode decomposition method and its implementation, Computer Engineering and Applications, 32 (2006), 44-47.Google Scholar

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M. Rosa and M. L. Gandarias, Multiplier method and exact solutions for a density dependen reaction-diffusion equation, Applied Mathematics and Nonlinear Sciences, 1 (2016), 311-320. Google Scholar

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J. Shan and K. Wilson, Causality between trade and tourism: Empirical evidence from China, Applied Economics Letters, 8 (2001), 279-283. Google Scholar

[22]

E. Smeral and M. Wuger, Does complexity matter? Methods for improving forecasting accuracy in tourism: The case of Australia D, Journal of Travel Research, 44 (2005), 100-110. Google Scholar

[23]

H. Song and G. Li., Tourism demand modelling and forecasting—A review of recent research, Tourism Management, 29 (2008), 203-220. Google Scholar

[24]

H. Song and S. F. Witt, Forecasting international tourist flows to Macau, Tourism Management, 27 (2006), 214-224. Google Scholar

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H. SongS. F. Witt and T. C. Jensen, Tourism forecasting: Accuracy of alternative econometric models, International Journal of Forecasting, 19 (2003), 123-141. Google Scholar

[26]

H. SongK. K. F. Wong and K. K. S. Chon, Modelling and forecasting the demand for Hong Kong tourism, International Journal of Hospitality Management, 22 (2003), 435-451. Google Scholar

[27]

J. Song, etal, Simulation model verification method based on empirical mode decomposition and gray relational analysis, Systems Engineering and Electronics, 35 (2013), 2613-2618.Google Scholar

[28]

W. Tao and M. Ni, A comparative study of tourism demand prediction in china and the west: Theoretical basis and model, Tourism Tribune, 8 (2010), 12-16. Google Scholar

[29]

C. TideswellT. Mules and B. Faulkner, An integrative approach to tourism forecasting: A glance in the rearview mirror, Journal of Travel Research, 40 (2001), 162-171. Google Scholar

[30]

Y. Wang, F. Li and Y. Zhang, et al., Prediction of Long-term Tourist Capacity in Wulong Scenic Spot Based on Combined Model, Mathematical Statistics and Management, 30 (2011), 770-779.Google Scholar

[31]

F. Wang and W. Zheng, A study of chinese international inbound tourism demand fluctuation based on ARIMA and GARCH, Industrial Economy, 7 (2009), 53-57. Google Scholar

[32]

W. Yu and C. Mu, Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks, Transportation Research, 2012, 152-154.Google Scholar

[33]

X. ZhaoL. Wang and H. Zou, Overview of prediction methods of international tourism demand in tourist destination countries, Tourism Tribune, 6 (1996), 27-29. Google Scholar

show all references

References:
[1]

G. E. P. Box and G. M. Jenkins, Time series analysis, forecasting and control, San Francisco: Holden Day, 1970. Google Scholar

[2]

A. CarreñoA. Vidal-FerrándizD. Ginestar and G. Verdú, Multilevel method to compute the lambda modes of the neutron diffusion equation, Applied Mathematics and Nonlinear Sciences, 2 (2017), 225-236. Google Scholar

[3]

F. ChanC. Lim and M. McAleer, Modelling multivariate international tourism demand and volatility, Tourism Management, 26 (2005), 459-471. Google Scholar

[4]

V. Cho, Tourism forecasting and its relationship with leading economic indicators, Journal of Hospitality and Tourism Research, 25 (2001), 399-420. Google Scholar

[5]

Y. Deng, etal, The processing of boundary problem in EMD method and Hilbert transform, Chinese Science Bulletin, 46 (2001), 257-263.Google Scholar

[6]

J. Gao, Principle of artificial neural network and simulation example, Beijing: China Machine Press, 2003, 1-2.Google Scholar

[7]

C. Goh and R. Law, Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention, Tourism Management, 23 (2002), 499-510. Google Scholar

[8]

W. Guo and J. Li, Tourism demand prediction based on improved optimized combined method, Statistics and Decision, 8 (2011), 75-77. Google Scholar

[9]

X. Guo and L. Wang, New algorithm and application of empirical mode decomposition, Noise and Vibration Prediction, 5 (2008), 70-71. Google Scholar

[10]

N. Huang, etal, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non - stationary time series analysis, Proceedings of the Royal Society of London, 454 (1998), 903-995. doi: 10.1098/rspa.1998.0193. Google Scholar

[11]

N. Kulendran and K. Wilson, Modelling business travel, Tourism Economics, 6 (2000), 47-59. Google Scholar

[12]

N. Kulendran and S. F. & Witt, Leading indicator tourism forecasts, Tourism Management, 24 (2003b), 503-510.Google Scholar

[13]

R. Law, A neural network model to forecast Japanese demand for travel to Hong Kong, Tourism Management, 20 (1999), 89-97. Google Scholar

[14]

K. Lei and Y. Chen, Chinese inbound tourist capacity prediction based on bp neural network and arima combined model, Tourism Tribune, 22 (2007), 20-25. Google Scholar

[15]

K. Lei and Y. Chen, Prediction of chinese inbound tourist capacity based on bp neural network and ARIMA combined model, Tourism Tribune, 22 (2007), 20-25. Google Scholar

[16]

G. LiK. F. WongH. Song and S. F. Witt, Tourism demand forecasting: A time varying parameter error correction model, Journal of Travel Research, 45 (2006), 175-185. Google Scholar

[17]

C. Lim and M. McAleer, Cointegration analysis of quarterly tourism demand by Hong Kong and Singapore for Australia, Applied Economics, 33 (2001a), 1599-1619. Google Scholar

[18]

X. Liu, Improvement and Application of BP Algorithm, Taiyuan: Taiyuan University of Technology, 2012.Google Scholar

[19]

H. Liu, etal, Empirical mode decomposition method and its implementation, Computer Engineering and Applications, 32 (2006), 44-47.Google Scholar

[20]

M. Rosa and M. L. Gandarias, Multiplier method and exact solutions for a density dependen reaction-diffusion equation, Applied Mathematics and Nonlinear Sciences, 1 (2016), 311-320. Google Scholar

[21]

J. Shan and K. Wilson, Causality between trade and tourism: Empirical evidence from China, Applied Economics Letters, 8 (2001), 279-283. Google Scholar

[22]

E. Smeral and M. Wuger, Does complexity matter? Methods for improving forecasting accuracy in tourism: The case of Australia D, Journal of Travel Research, 44 (2005), 100-110. Google Scholar

[23]

H. Song and G. Li., Tourism demand modelling and forecasting—A review of recent research, Tourism Management, 29 (2008), 203-220. Google Scholar

[24]

H. Song and S. F. Witt, Forecasting international tourist flows to Macau, Tourism Management, 27 (2006), 214-224. Google Scholar

[25]

H. SongS. F. Witt and T. C. Jensen, Tourism forecasting: Accuracy of alternative econometric models, International Journal of Forecasting, 19 (2003), 123-141. Google Scholar

[26]

H. SongK. K. F. Wong and K. K. S. Chon, Modelling and forecasting the demand for Hong Kong tourism, International Journal of Hospitality Management, 22 (2003), 435-451. Google Scholar

[27]

J. Song, etal, Simulation model verification method based on empirical mode decomposition and gray relational analysis, Systems Engineering and Electronics, 35 (2013), 2613-2618.Google Scholar

[28]

W. Tao and M. Ni, A comparative study of tourism demand prediction in china and the west: Theoretical basis and model, Tourism Tribune, 8 (2010), 12-16. Google Scholar

[29]

C. TideswellT. Mules and B. Faulkner, An integrative approach to tourism forecasting: A glance in the rearview mirror, Journal of Travel Research, 40 (2001), 162-171. Google Scholar

[30]

Y. Wang, F. Li and Y. Zhang, et al., Prediction of Long-term Tourist Capacity in Wulong Scenic Spot Based on Combined Model, Mathematical Statistics and Management, 30 (2011), 770-779.Google Scholar

[31]

F. Wang and W. Zheng, A study of chinese international inbound tourism demand fluctuation based on ARIMA and GARCH, Industrial Economy, 7 (2009), 53-57. Google Scholar

[32]

W. Yu and C. Mu, Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks, Transportation Research, 2012, 152-154.Google Scholar

[33]

X. ZhaoL. Wang and H. Zou, Overview of prediction methods of international tourism demand in tourist destination countries, Tourism Tribune, 6 (1996), 27-29. Google Scholar

Figure 1.  Schematic diagram of the combinatorial model based on empirical modal decomposition- error backpropagation artificial neural network
Figure 2.  Structure map of error backpropagation of artificial neural network
Figure 3.  2010 Mount Emei tourist area daily visitors capacity of the time series data
Figure 4.  Comparison between the estimated value and the actual value of the tourism carrying capacity using the empirical modal decomposition-error backpropagation
Figure 5.  Estimation error comparison between the empirical modal decomposition - error backpropagation artificial neural network prediction model and single error backpropagation artificial neural network prediction model
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