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FOA-ESN in tourism demand forecasting from the perspective of sustainable development

Author

Listed:
  • Xin Yan
  • Jianlei Han

Abstract

Nowadays, the tourism industry has made significant contributions to the national economy, and accurately predicting tourism demand is a necessary step to promote the rational allocation of tourism resources and sustainable development. Echo state network (ESN) is an algorithmic model that can effectively handle nonlinear problems. This study first adaptively adjusts the fruit fly optimisation algorithm (FOA) method and obtains the improved fruit fly optimisation algorithm (IFOA). Then, integrate IFOA with ESN (IFOA-ESN). IFOA-ESN mainly utilises IFOA to obtain key parameters of ESN, improving the overall performance. Finally, the simulation results of IFOA-ESN on tourism demand show that the average absolute percentage error (MAPE) and normalised root mean square error (NRMSE) values of IFOA-ESN are 0.40% and 0.61%, respectively, and their prediction accuracy is higher than other models. The predicted results obtained can serve as a reference for resource allocation and related policy decisions in the tourism industry.

Suggested Citation

  • Xin Yan & Jianlei Han, 2024. "FOA-ESN in tourism demand forecasting from the perspective of sustainable development," International Journal of Knowledge-Based Development, Inderscience Enterprises Ltd, vol. 14(1), pages 39-56.
    • Handle: RePEc:ids:ijkbde:v:14:y:2024:i:1:p:39-56
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