Combining the power of artificial intelligence and mathematical modelling: A hybrid technique for enhanced forecast of tourism receipts
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Varna Univ Management-Vum
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Despite being one of the most visited countries in the world, Turkiye's share of tourism revenue does not rank among the top ten. Therefore, it would be worth researching tourist expenditures and analysing this data could provide valuable insights. This research develops a novel approach to estimating and modelling tourism receipts by analysing expenditure types. Artificial intelligence-based methods, such as machine learning, have been increasingly used in the tourism literature to improve various aspects of the industry. However, little research has been conducted using a hybrid method to model and estimate tourist expenditure. This paper is the first to combine conventional mathematical analysis, specifically first-order two-variable polynomial equations, with artificial intelligence-based machine learning algorithms in a tourism setting. The research results indicate that expenditure types such as accommodation and food & beverage significantly impact Turkiye's tourism revenue and Turkiye's total tourism revenue will not exceed 45 billion dollars by 2027. This study provides a valuable and practical contribution to improving the accuracy and efficiency of methods for managing tourism economics, particularly in European countries where the economy heavily relies on income generated by tourism. Additionally, it fills a gap in studies focused on tourists' expenditure types by combining artificial intelligence and traditional analysis, making it a unique piece of research.
Açıklama
Anahtar Kelimeler
Artificial Intelligence, Machine Learning, Correlation-Based Feature Selection, Random Forest Algorithm, Tourist Expenditure Types
Kaynak
European Journal of Tourism Research
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
36
