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Demographic forecast modelling using SSA-XGBoost for smart population management based on multi-sources data.

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This study introduces a novel machine learning model combining mobile big data and traditional statistics for accurate sub-national population forecasting. The SSA-XGBoost model enhances demographic data prediction for smart population management.

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Area of Science:

  • Demography
  • Data Science
  • Computational Social Science

Background:

  • Accurate sub-national population forecasting is crucial for socio-economic planning.
  • Traditional methods face challenges with high population density and mobility, leading to forecast errors.
  • Integrating dynamic data sources can improve population prediction accuracy and real-time monitoring.

Purpose of the Study:

  • To develop and validate a combination model for accurate sub-national population forecasting.
  • To leverage machine learning, specifically Extreme Gradient Boosting (XGBoost), for demographic data prediction.
  • To enhance XGBoost model performance using the Sparrow Search Algorithm (SSA) for optimized parameter tuning.

Main Methods:

  • Utilized a multi-source dataset combining traditional statistical data with mobile communication big data (e.g., mobile phone signals).
  • Employed the Extreme Gradient Boosting (XGBoost) model as the base for population dynamic monitoring.
  • Optimized XGBoost parameters using the Sparrow Search Algorithm (SSA) to improve forecast accuracy.
  • Validated the model using data from national population censuses and mobile signal data in Hebei Province.

Main Results:

  • The SSA-XGBoost model achieved superior prediction performance, evidenced by a high R² of 0.9984.
  • Demonstrated significantly lower error metrics, including a mean absolute error of 0.0002 and a mean squared error of 6.9184.
  • Comparative experiments and cross-validation confirmed the model's effectiveness in forecasting demographic data (mortality, migration) by age and gender.

Conclusions:

  • The proposed SSA-XGBoost predictive model effectively forecasts demographic data for sub-national regions.
  • This approach enables more accurate and real-time smart population management.
  • The integration of mobile big data with advanced machine learning offers a robust solution for demographic forecasting challenges.