IDEAS home Printed from https://ideas.repec.org/a/igg/rmj000/v37y2024i1p1-21.html
   My bibliography  Save this article

Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization

Author

Listed:
  • Rui Luan

    (Shenyang Polytechnic College, China)

  • Ping Xu

    (Xihua University, China)

Abstract

In today's society, rural areas face challenges such as complex terrain and uneven population distribution, and infrastructure construction is exceptionally difficult. At the same time, poor information transmission and low communication efficiency have also become a major obstacle to the promotion of the digital economy in rural areas. This study aims to use gradient advancement models to identify potential risks in the growth of the digital economy sector related to rural revitalization. In this study, we used an enhanced hierarchical gradient boosting algorithm. The research results indicate that the introduction of this technology can provide us with a more comprehensive and reliable risk prediction model, thereby more scientifically assisting the development and decision-making of the digital economy in rural areas. This article provides a new perspective and solutions for development issues in rural areas, promoting sustainable development and economic growth in rural areas.

Suggested Citation

  • Rui Luan & Ping Xu, 2024. "Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization," Information Resources Management Journal (IRMJ), IGI Global, vol. 37(1), pages 1-21, January.
  • Handle: RePEc:igg:rmj000:v:37:y:2024:i:1:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IRMJ.343095
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Using Survey Data to Forecast Real Activity with Evolutionary Algorithms. a Cross-Country Analysis," Journal of Applied Economics, Taylor & Francis Journals, vol. 20(2), pages 329-349, November.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    3. Chengkai Zhang & Yanjun Zhang & Yu Li & Shan Li, 2023. "Coupling Coordination between Fintech and Digital Villages: Mechanism, Spatiotemporal Evolution and Driving Factors—An Empirical Study Based on China," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    4. Vadlamudi, Siddhartha, 2020. "The Impacts of Machine Learning in Financial Crisis Prediction," Asian Business Review, Asian Business Consortium, vol. 10(3), pages 171-176.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Blanchflower, David G. & Bryson, Alex, 2021. "The Economics of Walking About and Predicting Unemployment," GLO Discussion Paper Series 922, Global Labor Organization (GLO).
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2020. "“Spectral analysis of business and consumer survey data”," AQR Working Papers 2012002, University of Barcelona, Regional Quantitative Analysis Group, revised May 2020.
    3. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    4. Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2021. ""Nowcasting and forecasting GDP growth with machine-learning sentiment indicators"," IREA Working Papers 202103, University of Barcelona, Research Institute of Applied Economics, revised Feb 2021.
    6. Blanchflower, David G. & Bryson, Alex, 2023. "Labour Market Expectations and Unemployment in Europe," IZA Discussion Papers 15905, Institute of Labor Economics (IZA).
    7. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    9. Mustafa Ozguven & Chong Yan Gao & Mohamed Yacine Si Tayeb, 2021. "The Utilization of Autoregressive Forecasting Models in Strategic Management," International Journal of Science and Business, IJSAB International, vol. 5(7), pages 170-185.
    10. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 369-385, January.
    11. Yuan Zhao & Xinyang Wu, 2023. "The Spatiotemporal Relationship between Tourism Eco-Efficiency and Economic Resilience from Coupling Perspectives in China," Sustainability, MDPI, vol. 15(13), pages 1-17, June.
    12. Chengkai Zhang & Yu Li & Lili Yang & Zheng Wang, 2023. "Does the Development of Digital Inclusive Finance Promote the Construction of Digital Villages?—An Empirical Study Based on the Chinese Experience," Agriculture, MDPI, vol. 13(8), pages 1-23, August.
    13. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    14. José A. Tenreiro Machado & Maria Eugénia Mata & António M. Lopes, 2020. "Fractional Dynamics and Pseudo-Phase Space of Country Economic Processes," Mathematics, MDPI, vol. 8(1), pages 1-17, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:rmj000:v:37:y:2024:i:1:p:1-21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.