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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Regional Population Forecast and Analysis Based on Machine Learning Strategy.

Chian-Yue Wang1, Shin-Jye Lee2

  • 1Graduate Institute of Urban Planning, National Taipei University, Taipei 237, Taiwan.

Entropy (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach for objective regional population forecasting, moving beyond traditional methods. The XGBoost algorithm provides accurate, unbiased population growth predictions for urban planning.

Keywords:
boosting regressionpopulation growth prediction

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Area of Science:

  • Demography
  • Urban Planning
  • Machine Learning

Background:

  • Traditional regional population forecasting relies on demographer opinions and the Interregional Cohort-Component model.
  • The Cohort-Component model's reliance on planner-specified demographic rates can introduce bias and affect forecasting accuracy.

Purpose of the Study:

  • To propose and evaluate a novel machine learning-based method for objective multi-regional population forecasting.
  • To analyze and forecast population growth in major Taiwanese cities using advanced algorithms.
  • To provide an objective reference for urban and regional planning.

Main Methods:

  • Utilizing machine learning, specifically the XGBoost algorithm, for population growth analysis.
  • Applying feature importance evaluation within the XGBoost framework for objective insights.
  • Forecasting multi-regional population dynamics for present and near-future periods.

Main Results:

  • The machine learning method offers an objective alternative to traditional population forecasting techniques.
  • XGBoost algorithm effectively forecasts multi-regional population growth with evaluated feature importance.
  • The study provides objective data crucial for informed urban and regional planning.

Conclusions:

  • Machine learning, particularly XGBoost, can significantly improve the objectivity and accuracy of regional population forecasts.
  • This approach mitigates biases inherent in traditional demographic forecasting methods.
  • The findings offer valuable, data-driven insights for effective national infrastructure development and population management.