English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 56088/90157 (62%)
造访人次 : 11547922      在线人数 : 72
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU 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


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118372


    题名: Using Data Mining Techniques to Identify Enterprises with High Water Pollution Potential
    作者: Lo, H.C.;Huang, Y.Y.;Hu, L.S.
    关键词: Industrial Waste Pollution;Logistic Regression;Neural Network
    日期: 2019-10
    上传时间: 2020-03-21 12:11:07 (UTC+8)
    摘要: To stop environmental pollution caused by improper wastewater discharges, the related enterprises are required to regularly report the water monitoring data via Internet. To make sure the data submitted are in good quality, the inspectors are arranged to carry out the checkup on site either through random samplings or the appeals and reports from the public. Those enterprises violating the regulations thus make improvements as per the inspection results. However, in view of the limited manpower, the environmental control institutions will not be able to detect all the violators immediately if simply random samplings are applied. It is, therefore, an important issue to effectively and at the primal time to single out the enterprises with high water pollution potential. Using scientific data analysis, this paper aims to identify these targeting enterprises and construct a pollution warning model. Current report data on the Internet will be employed for statistic and data mining analysis, such as logistic regression and neural network. Meanwhile, comparisons will also be made concerning the validities of these warning models, hoping to better choose the appropriate one. The study findings can be used as basis for environmental control institutions while establishing pollution warning web portals so that the enforcement authorities can keep good track of the potential targets in advance and thus enhance the accuracy of the inspection.
    显示于类别:[企業管理學系暨研究所] 會議論文

    文件中的档案:

    档案 大小格式浏览次数
    index.html0KbHTML7检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈