Determination and Evaluation of Parameters Affecting Tourism Revenue by Machine Learning Methods

Abstract

The main focus of this chapter is to examine the tourism income of Türkiye as a case country, taking into account the structure of the tourism industry and relevant economic and social indicators. Statistical methods are used to investigate the factors that influence tourism income and to demonstrate the impact of these variables. The chapter aims to identify the key factors that should be considered when planning tourism-related activities and to explore the suitability of different models for future predictions. In addition, the chapter explores the use of machine learning models, such as artificial neural networks (ANN) and gradient boosted regression trees (GBRT), to compare their performance with the established multiple linear regression model. Furthermore, the chapter adds to the existing literature on tourism economics and forecasting methods by examining the performance of different models in predicting tourism income and highlighting the importance of factors such as the country's image, safety, and transportation opportunities in shaping tourism income in Türkiye.