基于机器学习算法的沙特阿拉伯GDP增长驱动因素

IF 1.7 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES
Mohamed F. Abd El-Aal , Mansour Shrahili , Mohamed Kayid , Shahid Mohammad
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引用次数: 0

摘要

本研究利用机器学习算法来调查对沙特阿拉伯经济增长率影响最大的经济部门,重点是农业、工业和服务业。分析表明,随机森林算法在识别驱动经济增长的关键部门方面提供了最高的预测精度。研究结果显示,服务业和工业对沙特GDP增长的贡献率分别为39.3%和37.7%。这些结果表明,这个国家正朝着经济多元化的方向迈进,因为它越来越依赖非石油部门的增长。尽管农业目前占GDP总量的比例较低,只有23%,但其相对较小的份额并不限制其扩张的潜力。这篇论文强调了农业发展,例如改进的技术和更有效的方法,如何能够增加经济影响。农业部门有潜力在促进未来经济增长方面发挥重要作用,这将进一步帮助沙特阿拉伯实现可持续增长和多样化的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GDP growth drivers in Saudi Arabia based on machine learning algorithms
This study utilizes machine-learning algorithms to investigate the economic sectors that most significantly influence Saudi Arabia's economic growth rate, focusing on agriculture, industry, and services. The analysis shows that the random forest algorithm offers the highest predictive accuracy in identifying the key sectors driving economic growth. The research findings show that the service and industrial sectors account for 39.3% and 37.7% of Saudi Arabia's GDP growth, respectively. These results show that this country is moving significantly toward diversifying its economy as it depends more and more on non-oil sectors for growth. Even while the agricultural industry presently makes up a lower 23% of the total GDP, its comparison small share does not limit its potential for expansion. The paper emphasizes how agricultural developments, such as enhanced technologies and more efficient methods, could increase economic impact. The agricultural sector has the potential to play a significant role in boosting future economic growth, which would further help Saudi Arabia's objectives for sustainable growth and diversification.
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来源期刊
自引率
5.90%
发文量
130
审稿时长
16 weeks
期刊介绍: Journal of Radiation Research and Applied Sciences provides a high quality medium for the publication of substantial, original and scientific and technological papers on the development and applications of nuclear, radiation and isotopes in biology, medicine, drugs, biochemistry, microbiology, agriculture, entomology, food technology, chemistry, physics, solid states, engineering, environmental and applied sciences.
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