Machine Learning-Based Tourism Demand Prediction Using Tourism Instability Indicators

This study presents a data-driven framework for analyzing the relationship between geopolitical risk and tourism demand using the Saudi Arabia Tourism Dataset (2015–2024). Due to the absence of explicit geopolitical indicators, the research constructs a tourism-instability proxy index derived from tourism volatility, spending fluctuations, and disruption periods to represent uncertainty conditions affecting tourism demand, including volatility in tourist arrivals, spending fluctuations, and shock indicators such as disruption periods. A two-stage machine learning pipeline is implemented to (i) predict the engineered GRI and (ii) evaluate its impact on tourism demand, measured by tourist numbers. Five algorithms—Extra Trees, Gradient Boosting, AdaBoost, K-Nearest Neighbors, and Ridge Regression—are applied and evaluated using MAE, RMSE, and R2. In Stage 1, predicting the GRI remains challenging, with the best model (Extra Trees) achieving R2 = 0.184, MAE = 0.168, and RMSE = 0.209, reflecting the complexity of modeling indirectly constructed risk. In Stage 2, performance improves significantly, with Extra Trees achieving R2 = 0.577, MAE = 1120.57, and RMSE = 2749.10, demonstrating strong predictive capability. The findings suggest that instability-sensitive tourism indicators may contain predictive information associated with tourism demand variations. Compared to existing studies that rely on external risk indices or normalized datasets, this research contributes a novel approach by engineering risk directly from tourism data and validating its predictive influence. The proposed framework provides practical insights for policymakers and tourism planners in managing uncertainty and supporting sustainable tourism development under dynamic geopolitical conditions.