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

Area of Science:

• Population dynamics
• Grey systems theory
• Sustainable development

Background:

• Population, resources, and environment are interdependent.
• Accurate population forecasting is vital for sustainable societal development.
• Population systems are complex dynamic grey systems influenced by numerous factors.

Purpose of the Study:

• To develop a more accurate grey population prediction model.
• To improve the description of population system evolution laws.
• To enhance the model's ability to capture data dynamics and robustness.

Main Methods:

• Introduction of a damping accumulated operator.
• Construction of a grey population prediction model based on the nonlinear grey Bernoulli model.
• Optimization of model parameters using a whale optimization algorithm.

Main Results:

• The damping accumulated-nonlinear grey Bernoulli model demonstrated higher fitting and prediction accuracy compared to traditional grey models.
• The new operator effectively captures dynamic changes and weakens data randomness.
• The model showed enhanced robustness against external interference factors.

Conclusions:

• The novel damping accumulated-nonlinear grey Bernoulli model is a simple and effective tool for population prediction.
• This method accurately grasps future population trends.
• The study expands the application scope of grey prediction models.