武汉市人口预测模型的比较研究

Ping Hu, Shunkang Yan
{"title":"武汉市人口预测模型的比较研究","authors":"Ping Hu, Shunkang Yan","doi":"10.1109/CESCE.2010.273","DOIUrl":null,"url":null,"abstract":"This paper uses linear prediction model, the natural growth model and the exponential growth model to fitted Wuhan population quantity from 1978 to 2004, and then according to the determine corresponding parameters, predicted the population quantity from 2005 to 2007 and compared, the conclusion is that the results of natural growth model and the exponential growth model are better.","PeriodicalId":6371,"journal":{"name":"2010 International Conference on Challenges in Environmental Science and Computer Engineering","volume":"35 1","pages":"22-25"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Comparative Study of Wuhan Population Prediction Models\",\"authors\":\"Ping Hu, Shunkang Yan\",\"doi\":\"10.1109/CESCE.2010.273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper uses linear prediction model, the natural growth model and the exponential growth model to fitted Wuhan population quantity from 1978 to 2004, and then according to the determine corresponding parameters, predicted the population quantity from 2005 to 2007 and compared, the conclusion is that the results of natural growth model and the exponential growth model are better.\",\"PeriodicalId\":6371,\"journal\":{\"name\":\"2010 International Conference on Challenges in Environmental Science and Computer Engineering\",\"volume\":\"35 1\",\"pages\":\"22-25\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Challenges in Environmental Science and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CESCE.2010.273\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Challenges in Environmental Science and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CESCE.2010.273","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文采用线性预测模型、自然增长模型和指数增长模型对武汉市1978 - 2004年的人口数量进行拟合,并根据确定的相应参数,对2005 - 2007年的人口数量进行预测和比较,结论是自然增长模型和指数增长模型的结果较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The Comparative Study of Wuhan Population Prediction Models
This paper uses linear prediction model, the natural growth model and the exponential growth model to fitted Wuhan population quantity from 1978 to 2004, and then according to the determine corresponding parameters, predicted the population quantity from 2005 to 2007 and compared, the conclusion is that the results of natural growth model and the exponential growth model are better.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Recent Advances in Managed Aquifer Recharge in China Minimum Attribute Number in Decision Table Based on Maximum Entropy Principle A Security Architecture for Wireless Mesh Network The Research of K-means Clustering Algorithm Based on Association Rules Improving the Identification of Business Components: A Framework Based on Knowledge Management of Process Illustrating
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1