Machine Learning and Artificial Intelligence for a Sustainable Tourism: A Case Study on Saudi Arabia

Author:

Louati Ali1ORCID,Louati Hassen2,Alharbi Meshal3ORCID,Kariri Elham1ORCID,Khawaji Turki1,Almubaddil Yasser1,Aldwsary Sultan1

Affiliation:

1. Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

2. Computer Science, Kingdom University, Riffa P.O. Box 40434, Bahrain

3. Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

Abstract

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond.

Funder

Prince Sattam Bin Abdulaziz University

Kingdom University

Publisher

MDPI AG

Reference72 articles.

1. World Tourism Organization (UNWTO) (2018). UNWTO Tourism Highlights, UNWTO. [2018th ed.].

2. Kingdom of Saudi Arabia (2024, June 11). Saudi Vision 2030, Available online: https://www.vision2030.gov.sa.

3. World Travel & Tourism Council (2020). Travel & Tourism: Economic Impact 2020, World Travel & Tourism Council. Technical Report.

4. International Monetary Fund (2020). World Economic Outlook, October 2020: A Long and Difficult Ascent, International Monetary Fund. Technical Report.

5. World Tourism Organization (UNWTO) (2021). UNWTO Tourism Highlights, UNWTO. [2021st ed.].

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