Title: A study on the economic and sustainable development forecast of rural tourism industry based on ANN

Authors: Li Huang; Jingwei Zhai

Addresses: Party and Government Office, Guilin Tourism University, Guilin, 541006, China ' College of Marxism, Nanning College of Technology, Guilin, 541006, China

Abstract: This study establishes a rural tourism industry economic sustainability prediction model based on the back propagation neural network (BP) in artificial neural network (ANN). It selects the indicators that have a large influence on the rural tourism industry economic sustainability prediction, and takes the four indicators with the highest weight percentage as the input of the prediction model, and verify the validity of the model. The result shows that the average relative prediction error of the univariate BP neural network was smaller than the grey model (GM). The average absolute value of relative prediction error for the multivariate BP neural network was smaller than the prediction error value of the univariate BP neural network model. The AUC value of the multivariate BP prediction model based on this study is 0.93. This research model improves the accuracy of predicting the sustainable economic development of the rural tourism industry.

Keywords: artificial neural network; ANN; rural tourism; BP neural network; economic forecasting; sustainable development.

DOI: 10.1504/IJWET.2023.133610

International Journal of Web Engineering and Technology, 2023 Vol.18 No.3, pp.169 - 184

Received: 14 Sep 2022
Received in revised form: 30 Jan 2023
Accepted: 21 Feb 2023
Published online: 25 Sep 2023 *

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