IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v20y2024i1p1-19.html
   My bibliography  Save this article

Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer

Author

Listed:
  • Feiqi Liu

    (School of Information Science and Engineering, Hebei North University, China)

  • Dong Yang

    (School of Information Science and Engineering, Hebei North University, China)

  • Yuyang Zhang

    (College of Robotics Science and Engineering, Northeastern University, China)

  • Chengcai Yang

    (Zhuolu County Animal Husbandry and Fishery Service Center, China)

  • Jingjing Yang

    (School of Information Science and Engineering, Hebei North University, China)

Abstract

The rabbit breeding industry exhibits vast economic potential and growth opportunities. Nevertheless, the ineffective prediction of environmental conditions in rabbit houses often leads to the spread of infectious diseases, causing illness and death among rabbits. This paper presents a multi-parameter predictive model for environmental conditions such as temperature, humidity, illumination, CO2 concentration, NH3 concentration, and dust conditions in rabbit houses. The model adeptly distinguishes between day and night forecasts, thereby improving the adaptive adjustment of environmental data trends. Importantly, the model encapsulates multi-parameter environmental forecasting to heighten precision, given the high degree of interrelation among parameters. The model's performance is assessed through RMSE, MAE, and MAPE metrics, yielding values of 0.018, 0.031, and 6.31% respectively in predicting rabbit house environmental factors. Experimentally juxtaposed with Bert, Seq2seq, and conventional transformer models, the method demonstrates superior performance.

Suggested Citation

  • Feiqi Liu & Dong Yang & Yuyang Zhang & Chengcai Yang & Jingjing Yang, 2024. "Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 20(1), pages 1-19, January.
  • Handle: RePEc:igg:jdwm00:v:20:y:2024:i:1:p:1-19
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.336286
    Download Restriction: no
    ---><---

    More about this item

    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:igg:jdwm00:v:20:y:2024:i:1:p:1-19. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.