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An Optimized Damping Grey Population Prediction Model and Its Application on China’s Population Structure Analysis

Author

Listed:
  • Xiaojun Guo

    (School of Science, Nantong University, Nantong 226019, China)

  • Rui Zhang

    (School of Science, Nantong University, Nantong 226019, China)

  • Houxue Shen

    (School of Science, Nantong University, Nantong 226019, China)

  • Yingjie Yang

    (Institute of Artificial Intelligence, De Montfort University, Leicester LE1 9BH, UK)

Abstract

Population, resources and environment constitute an interacting and interdependent whole. Only by scientifically forecasting and accurately grasping future population trends can we use limited resources to promote the sustainable development of society. Because the population system is affected by many complex factors and the structural relations among these factors are complex, it can be regarded as a typical dynamic grey system. This paper introduces the damping accumulated operator to construct the grey population prediction model based on the nonlinear grey Bernoulli model in order to describe the evolution law of the population system more accurately. The new operator can give full play to the principle of new information first and further enhance the ability of the model to capture the dynamic changes of the original data. A whale optimization algorithm was used to optimize the model parameters and build a smooth prediction curve. Through three practical cases related to the size and structure of the Chinese population, the comparison with other grey prediction models shows that the fitting and prediction accuracy of the damping accumulated–nonlinear grey Bernoulli model is higher than that of the traditional grey prediction model. At the same time, the damping accumulated operator can weaken the randomness of the original data sequence, reduce the influence of external interference factors, and enhance the robustness of the model. This paper proves that the new method is simple and effective for population prediction, which can not only grasp the future population change trend more accurately but also further expand the application range of the grey prediction model.

Suggested Citation

  • Xiaojun Guo & Rui Zhang & Houxue Shen & Yingjie Yang, 2022. "An Optimized Damping Grey Population Prediction Model and Its Application on China’s Population Structure Analysis," IJERPH, MDPI, vol. 19(20), pages 1-25, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13478-:d:945901
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    References listed on IDEAS

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    1. Lixuan Chen & Tianyu Mu & Xiuting Li & Jichang Dong, 2022. "Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models," Sustainability, MDPI, vol. 14(8), pages 1-23, April.
    2. Jianzhou Wang & Pei Du, 2021. "Quarterly PM2.5 prediction using a novel seasonal grey model and its further application in health effects and economic loss assessment: evidences from Shanghai and Tianjin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 889-909, May.
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    Cited by:

    1. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).

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