Journal Article

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

Khawaji, Turki, Louati, Ali, Louati, Hassen, Aldwsary, Sultan, Alharbi, Meshal, Almubaddil, Yasser, Kariri, Elham

2024

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Artificial intelligence / Autoregressive models / Bibliometrics / Blockchain / Case studies / Computational linguistics / COVID-19 / Data analysis / Data collection / Data entry / Decision trees / Deep learning / Economic forecasting / Environmental management / Epidemics / Forecasting / Forecasts and trends / Information management / Information storage / Language processing / Machine learning / Medical research / Natural language interfaces / Pandemics / Prediction models / Predictive analytics / Recommender systems / Resilience / Resource management / Robustness / Sales promotions / Saudi Arabia / Search engines / Social networks / spending prediction / Statistical analysis / Strategic planning / sustainability / Sustainable development / Sustainable tourism / time series forecasting / Tourism / Travel / Travel industry / User generated content