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Regional Youth Population Prediction Using LSTM

Author

Listed:
  • Jaejun Seo

    (Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea)

  • Sunwoong Yoon

    (Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea)

  • Jiwoo Kim

    (Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea)

  • Kyusang Kwon

    (Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea)

Abstract

Regional shrinkage, driven by declining birth rates, an aging population, and population concentration in the capital region, has become an increasingly serious issue in South Korea, threatening the long-term sustainability of local communities. Among various factors, youth out-migration is a key driver, undermining the economic resilience and vitality of local areas. This study aims to predict youth population trends across 229 municipalities by incorporating diverse regional socioeconomic factors and providing a foundation for policy implementation to mitigate demographic disparities. To this end, a long short-term memory (LSTM) model, based on a direct approach that independently forecasts each future time point, was employed. The model was trained using the youth population data from 2003 to 2022 and socioeconomic variables, including employment, education, housing, and infrastructure. The results reveal a persistent nationwide decline in the youth population, with significantly sharper decreases in local areas than in the capital region. These findings underscore the deepening spatial imbalance and highlight the urgent need for region-specific demographic policies to address the accelerating risk of regional population decline.

Suggested Citation

  • Jaejun Seo & Sunwoong Yoon & Jiwoo Kim & Kyusang Kwon, 2025. "Regional Youth Population Prediction Using LSTM," Sustainability, MDPI, vol. 17(15), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:6905-:d:1712949
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    References listed on IDEAS

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    1. Schnaubelt, Matthias, 2019. "A comparison of machine learning model validation schemes for non-stationary time series data," FAU Discussion Papers in Economics 11/2019, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Deng, Ai, 2023. "Time series cross validation: A theoretical result and finite sample performance," Economics Letters, Elsevier, vol. 233(C).
    3. Yuanping Wang & Lang Hu & Lingchun Hou & Lin Wang & Juntao Chen & Yu He & Xinyue Su, 2024. "A SHAP machine learning-based study of factors influencing urban residents' electricity consumption - evidence from chinese provincial data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(12), pages 30445-30476, December.
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    Cited by:

    1. Feng Ge & Jiayu Liu & Laigen Jia & Gaixiang Chen & Changshun Wang & Yuetian Wang & Hongguang Chen & Fanhao Meng, 2025. "The Spatial Differentiation Characteristics of the Residential Environment Quality in Northern Chinese Cities: Based on a New Evaluation Framework," Sustainability, MDPI, vol. 17(16), pages 1-21, August.

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