Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model
文献类型：期刊论文
| 作者 | Li, Shuang1; Yang, Zewei1; Li, Hongsheng2 ; Shu, Guangwen3
|
| 刊名 | JOURNAL OF FORECASTING
 |
| 出版日期 | 2018-03-01 |
| 卷号 | 37期号:2页码:225-234 |
| 关键词 | fertility rate Leslie matrix model ls-SVM mortality rate population structure |
| ISSN号 | 0277-6693 |
| DOI | 10.1002/for.2486 |
| 通讯作者 | Shu, Guangwen(shuguangwen@whu.edu.cn) |
| 英文摘要 | China is a populous country that is facing serious aging problems due to the single-child birth policy. Debate is ongoing whether the liberalization of the single-child policy to a two-child policy can mitigate China's aging problems without unacceptably increasing the population. The purpose of this paper is to apply machine learning theory to the demographic field and project China's population structure under different fertility policies. The population data employed derive from the fifth and sixth national census records obtained in 2000 and 2010 in addition to the annals published by the China National Bureau of Statistics. Firstly, the sex ratio at birth is estimated according to the total fertility rate based on least squares regression of time series data. Secondly, the age-specific fertility rates and age-specific male/female mortality rates are projected by a least squares support vector machine (LS-SVM) model, which then serve as the input to a Leslie matrix model. Finally, the male/female age-specific population data projected by the Leslie matrix in a given year serve as the input parameters of the Leslie matrix for the following year, and the process is iterated in this manner until reaching the target year. The experimental results reveal that the proposed LS-SVM-Leslie model improves the projection accuracy relative to the conventional Leslie matrix model in terms of the percentage error and mean algebraic percentage error. The results indicate that the total fertility ratio should be controlled to around 2.0 to balance concerns associated with a large population with concerns associated with an aging population. Therefore, the two-child birth policy should be fully instituted in China. However, the fertility desire of women tends to be low due to the high cost of living and the pressure associated with employment, particularly in the metropolitan areas. Thus additional policies should be implemented to encourage fertility. |
| WOS关键词 | LSSVM MODEL ; GROWTH-RATE ; FERTILITY ; FORECASTS ; MORTALITY ; RATES |
| 资助项目 | National Natural Science Foundation of China[41421001] |
| WOS研究方向 | Business & Economics |
| 语种 | 英语 |
| WOS记录号 | WOS:000425088200006 |
| 出版者 | WILEY |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/57022]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Shu, Guangwen |
| 作者单位 | 1.Wuhan Univ, Int Sch Software, Wuhan, Hubei, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 3.South Cent Univ Nationalities, Sch Pharmaceut Sci, Wuhan 430074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Shuang,Yang, Zewei,Li, Hongsheng,et al. Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model[J]. JOURNAL OF FORECASTING,2018,37(2):225-234. |
| APA | Li, Shuang,Yang, Zewei,Li, Hongsheng,&Shu, Guangwen.(2018).Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model.JOURNAL OF FORECASTING,37(2),225-234. |
| MLA | Li, Shuang,et al."Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model".JOURNAL OF FORECASTING 37.2(2018):225-234. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。