政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/120842
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 114205/145239 (79%)
造访人次 : 52686193      在线人数 : 946
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 >  Item 140.119/120842


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/120842


    题名: Improving the Accuracy and Efficiency of PM2.5 Forecast Service Using Cluster-Based Hybrid Neural Network Model
    作者: Mahajan, Sachit
    Liu, Hao-Min
    Tsai, Tzu-Chieh
    蔡子傑
    Chen, Ling-Jyh
    贡献者: 資科系
    关键词: Internet of Things;forecasting;smart cities;neural networks
    日期: 2018
    上传时间: 2018-11-07 17:05:17 (UTC+8)
    摘要: Information and communication technologies have been widely used to achieve the objective of smart city development. A smart air quality sensing and forecasting system is an important part of a smart city. One of the major challenges in designing such a forecast system is ensuring high accuracy and an acceptable computation time. In this paper, we show that it is possible to accurately forecast fine particulate matter (PM2.5) concentrations with low computation time by using different clustering techniques. An Internet of Things framework comprising of Airbox devices for PM2.5 monitoring has been used to acquire the data. Our main focus is to achieve high forecasting accuracy with reduced computation time. We use a hybrid model to do the forecast and a grid based system to cluster the monitoring stations based on the geographical distance. The experiments and evaluation is done using Airbox devices data from 557 stations deployed all over Taiwan. We are able to demonstrate that a proper clustering based on geographical distance can reduce the forecasting error rate and also the computation time. Also, in order to further evaluate our system, we have applied wavelet-based clustering to group the monitoring stations. A final comparative analysis is done for different clustering schemes with respect to accuracy and computational time.
    關聯: IEEE ACCESS, 6, 19193-19204
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1109/ACCESS.2018.2820164
    DOI: 10.1109/ACCESS.2018.2820164
    显示于类别:[資訊科學系] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML2657检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈