BRP-Net: A discrete-aware network based on attention mechanisms and LSTM for birth rate prediction in prefecture-level cities.

ABSTRACT: The continuous decline in the birth rate can lead to a series of social and economic problems. Accurately predicting the birth rate of a region will help national and local governments to formulate more scientifically sound development policies. This paper proposes a discrete-aware model BRP-Net based on attention mechanism and LSTM, for effectively predicting the birth rate of prefecture-level cities. BRP-Net is trained using multiple variables related to comprehensive development of prefecture-level cities, covering factors such as economy, education and population structure that can influence the birth rate. Additionally, the comprehensive data of China's prefecture-level cities exhibits strong spatiotemporal specificity. Our model leverages the advantages of attention mechanism to iden

SUBMITTER: Xue M 

PROVIDER: S-EPMC11392232 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature