Mohamed F. Abd El-Aal , Mansour Shrahili , Mohamed Kayid , Shahid Mohammad
{"title":"GDP growth drivers in Saudi Arabia based on machine learning algorithms","authors":"Mohamed F. Abd El-Aal , Mansour Shrahili , Mohamed Kayid , Shahid Mohammad","doi":"10.1016/j.jrras.2025.101380","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101380"},"PeriodicalIF":1.7000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Radiation Research and Applied Sciences","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1687850725000925","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.