用机器学习模型预测沙特阿拉伯旅游业的增长,以实现2030年愿景
Amjad Ghazai Alsulami1, Amal Alharbi1, Usman Ali Khan2
1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Scientific reports
|January 20, 2026
概括
机器学习模型准确预测沙特阿拉伯的旅游需求,这对2030年愿景的经济多样化至关重要. 集合方法,特别是VotingR2,显示出对可持续旅游规划和发展的强大预测能力.
科学领域:
- 数据科学数据科学数据科学
- 计量经济学 计量经济学
- 旅游学研究 旅游学研究
背景情况:
- 沙特阿拉伯的2030年愿景优先考虑旅游业,以实现超越石油的经济多样化.
- 准确的旅游需求预测对于可持续发展和基础设施规划至关重要.
研究的目的:
- 调查机器学习 (ML) 技术来预测沙特阿拉伯的城市级旅游者到来.
- 评估各种回归和整体ML模型的性能.
主要方法:
- 利用了2021-2023年城市级数据集.
- 评估随机森林,梯度提升,HistGradientBoosting和整体模型 (堆叠,投票).
- 雇员混合年培训,时间保持和时间序列交叉验证.
主要成果:
- 在所有评估场景中,VotingR2组合模型表现出卓越的性能.
- 获得的R2值为0.9601 (情景A),0.8735 (情景B) 和0.9578 (情景C).
- 使用Prophet和ML生成了2024-2034年的长期旅游需求预测.
结论:
- 集成的ML方法有效地捕捉到旅游需求的复杂,非线性模式.
- 调查结果为战略决策,基础设施优化和政策制定提供了可操作的见解.
- 支持沙特阿拉伯的2030年愿景目标,以实现多元化和可持续的旅游业.
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