IDEAS home Printed from https://ideas.repec.org/a/ags/asagre/290294.html
   My bibliography  Save this article

Application of Combined Prediction Model in Predicting Total Water Consumption in Ningxia

Author

Listed:
  • WANG,Yongliang
  • CHEN,JIongli
  • TANG,Lian

Abstract

The gray GM (1,1) prediction model and Logistic equation gray prediction model were established separately, and then the combined prediction model was established. Taking the water consumption in Ningxia Hui Autonomous Region from 2006 to 2012 as modeling data, the total water consumption of the whole region of Ningxia in 2018-2020 was analyzed and predicted. The results show that the accuracy of the three prediction models meets the accuracy requirements, but the gray GM(1,1) and combined prediction models better conform to the actual situation and have better applicability.

Suggested Citation

  • WANG,Yongliang & CHEN,JIongli & TANG,Lian, 2019. "Application of Combined Prediction Model in Predicting Total Water Consumption in Ningxia," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 11(03), March.
  • Handle: RePEc:ags:asagre:290294
    DOI: 10.22004/ag.econ.290294
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/290294/files/Application%20of%20Combined%20Prediction%20Model%20in%20Predicting%20Total%20Water%20Consumption%20in%20Ningxia.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.290294?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    Agribusiness;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:asagre:290294. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.