IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i5p755-d1599536.html
   My bibliography  Save this article

Reconstruction and Prediction of Regional Population Migration Neural Network Model with Age Structure

Author

Listed:
  • Cuiying Li

    (School of Mathematical Sciences, Bohai University, Jinzhou 121013, China)

  • Yulin Wu

    (School of Mathematical Sciences, Bohai University, Jinzhou 121013, China)

  • Yi Cheng

    (School of Mathematical Sciences, Bohai University, Jinzhou 121013, China)

  • Yandong Guo

    (School of Mathematical Sciences, Bohai University, Jinzhou 121013, China)

  • Kun Wei

    (Defense Innovation Institute, Academy of Military Science, Beijing 100071, China)

  • Jie Zhao

    (Defense Innovation Institute, Academy of Military Science, Beijing 100071, China)

Abstract

The rationale for age-structured population migration system models lies in the significant impact of age patterns on migration dynamics, as age-specific migration rates exhibit distinct regularities and are influenced by life course transitions, socio-economic conditions, and demographic structures. Based on artificial neural networks, this article proposes a class of population models with age structure described by partial differential equations to predict the future trends of regional population changes. The population migration rate, as a complex nonlinear feature, can be trained through artificial neural networks, providing a population approximation system. By employing semigroup theory, we establish the well-posedness of the proposed system. It is shown that the solution of the approximation system can converge to that of the original system in the sense of the L 2 -norm. Finally, several simulation experiments are provided to verify the effectiveness of the population forecasting model.

Suggested Citation

  • Cuiying Li & Yulin Wu & Yi Cheng & Yandong Guo & Kun Wei & Jie Zhao, 2025. "Reconstruction and Prediction of Regional Population Migration Neural Network Model with Age Structure," Mathematics, MDPI, vol. 13(5), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:755-:d:1599536
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/5/755/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/5/755/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bloom, David E. & Börsch-Supan, Axel & McGee, Patrick & Seike, Atsushi, 1970. "Population Aging: Facts, Challenges, and Responses," MEA discussion paper series 201224, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    2. Bruno Lanz & Simon Dietz & Timothy Swanson, 2017. "Global Population Growth, Technology, And Malthusian Constraints: A Quantitative Growth Theoretic Perspective," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 58(3), pages 973-1006, August.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," IREA Working Papers 201502, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
    4. Thach Ngoc Pham & Duc Hong Vo, 2021. "Aging Population and Economic Growth in Developing Countries: A Quantile Regression Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(1), pages 108-122, January.
    5. Xiaojun Guo & Rui Zhang & Naiming Xie & Jingliang Jin & Lifeng Wu, 2021. "Predicting the Population Growth and Structure of China Based on Grey Fractional-Order Models," Journal of Mathematics, Hindawi, vol. 2021, pages 1-11, July.
    6. Mingyu Tong & Zou Yan & Liu Chao, 2020. "Research on a Grey Prediction Model of Population Growth Based on a Logistic Approach," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-14, October.
    7. Bruno Lanz & Simon Dietz & Timothy Swanson, 2017. "Global Population Growth, Technology, And Malthusian Constraints: A Quantitative Growth Theoretic Perspective," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 58, pages 973-1006, August.
    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. Lucas Bretschger & Karen Pittel, 2020. "Twenty Key Challenges in Environmental and Resource Economics," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 77(4), pages 725-750, December.
    2. Bretschger, Lucas, 2024. "Green Road is open: Economic Pathway with a carbon price escalator," Journal of Environmental Economics and Management, Elsevier, vol. 127(C).
    3. Lucas Bretschger & Karen Pittel, 2019. "Twenty Key Questions in Environmental and Resource Economics," CER-ETH Economics working paper series 19/328, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    4. Jin, Zhangfeng & Pan, Shiyuan & Zheng, Zhijie, 2021. "The Unintended Consequences of Relaxing Birth Quotas: Theory and Evidence," GLO Discussion Paper Series 819, Global Labor Organization (GLO).
    5. Lehmann-Hasemeyer, Sibylle & Prettner, Klaus & Tscheuschner, Paul, 2023. "The scientific revolution and its implications for long-run economic development," World Development, Elsevier, vol. 168(C).
    6. Pedro Naso; Ozgun Haznedar; Bruno Lanz; Timothy Swanson, 2021. "Food Security in the Long-Run:A Macroeconomic Approach to Land Use Policy," CIES Research Paper series 71-2021, Centre for International Environmental Studies, The Graduate Institute.
    7. Naso, Pedro & Lanz, Bruno & Swanson, Tim, 2020. "The return of Malthus? Resource constraints in an era of declining population growth," European Economic Review, Elsevier, vol. 128(C).
    8. Bretschger, Lucas, 2020. "Malthus in the light of climate change," European Economic Review, Elsevier, vol. 127(C).
    9. Naso, Pedro & Haznedar, Ozgun & Lanz, Bruno & Swanson, Tim, 2022. "A macroeconomic approach to global land use policy," Resource and Energy Economics, Elsevier, vol. 69(C).
    10. Mário Santiago Céu & Raquel Medeiros Gaspar, 2022. "Vegetative cycle and bankruptcy predictors of agricultural firms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(12), pages 445-454.
    11. Brausmann, Alexandra & Bretschger, Lucas, 2018. "Economic development on a finite planet with stochastic soil degradation," European Economic Review, Elsevier, vol. 108(C), pages 1-19.
    12. Lupi, Veronica & Marsiglio, Simone, 2021. "Population growth and climate change: A dynamic integrated climate-economy-demography model," Ecological Economics, Elsevier, vol. 184(C).
    13. Peretto, Pietro & Valente, Simone, 2024. "Sustainable Growth and Secular Trends," MPRA Paper 120828, University Library of Munich, Germany.
    14. Bretschger, Lucas, 2024. "Energy transition and climate change abatement: A macroeconomic analysis," Resource and Energy Economics, Elsevier, vol. 76(C).
    15. Mónica L. Azevedo & Óscar Afonso & Sandra T. Silva, 2017. "Endogenous Growth and Intellectual Property Rights: A North–South Modelling Proposal with Population Ageing," Australian Economic Papers, Wiley Blackwell, vol. 56(1), pages 72-94, March.
    16. Mehri , N. & Messkoub, M. & Kunkel, S., 2019. "Trends, determinants and the implications of population aging in Iran," ISS Working Papers - General Series 646, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    17. Peter K. Kruse-Andersen, 2019. "Directed Technical Change, Environmental Sustainability, and Population Growth," Discussion Papers 19-12, University of Copenhagen. Department of Economics.
    18. Alicia H. Munnell & Geoffrey Sanzenbacher & Alice Zulkarnain, 2020. "What Factors Explain the Decline in Widowed Women’s Poverty?," Demography, Springer;Population Association of America (PAA), vol. 57(5), pages 1881-1902, October.
    19. Wang, Jinghua & Zhang, Zhao & Lu, Guangquan & Yu, Bin & Zhan, Chengyu & Cai, Jingsong, 2023. "Analyzing multiple COVID-19 outbreak impacts: A case study based on Chinese national air passenger flow," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    20. Sérgio Roberto Da Silva & Jairo José de Oliveira Andrade, 2022. "A Review on the Effect of Mechanical Properties and Durability of Concrete with Construction and Demolition Waste (CDW) and Fly Ash in the Production of New Cement Concrete," Sustainability, MDPI, vol. 14(11), pages 1-27, May.

    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:gam:jmathe:v:13:y:2025:i:5:p:755-:d:1599536. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.