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

Yanan Wang; Tao Xie; Xiaowen Jie

doi:10.3934/dcdss.2019056

Article Contents

2019, Volume 12, Issue 4&5: 837-847. Doi: 10.3934/dcdss.2019056

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

Received: September 2017

Revised: December 2017

Published: March 2019

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Abstract

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.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. Schematic diagram of the combinatorial model based on empirical modal decomposition- error backpropagation artificial neural network

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Figure 2. Structure map of error backpropagation of artificial neural network

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Figure 3. 2010 Mount Emei tourist area daily visitors capacity of the time series data

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Figure 4. Comparison between the estimated value and the actual value of the tourism carrying capacity using the empirical modal decomposition-error backpropagation

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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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