Modelling the Tourism Revenue of Türkiye Using Deep Learning Networks

Abstract

The gross domestic product of countries plays a key role in the development and wealth of nations. There are several components of the gross domestic product, such as industrial revenue, revenue from services, and tourism revenue. Türkiye is located in Anatolia, which is very rich from a historical viewpoint. Therefore, Türkiye attracts tourists from all over the world, making its tourism revenue an important contributor to its gross domestic product. This study aimed to model the tourism revenue of Türkiye using machine learning methods. In this study, the tourism revenue of Türkiye, dependent on the number of tourists, oil prices, and the exchange rate, are modelled for the period of 2008-2022. The data of these variables were taken from official sources, and then the causality analyses were carried out. As the next step, the tourism revenue is modelled as a function of the number of tourists, oil prices, and the exchange rate. A deep learning network is developed using the Python programming language for modelling the tourism revenue. The developed deep learning network is then trained using a portion of the data. The performance of the developed deep learning network is then evaluated using the performance metrics such as the coefficient of determination, mean absolute error, root means square error, and the mean absolute percentage error. These metrics show that the developed deep learning network successfully models the tourism revenue dependent on the number of tourists, oil prices, and the exchange rate.