IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i11d10.1007_s11269-017-1692-8.html
   My bibliography  Save this article

A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method

Author

Listed:
  • Shengzhi Huang

    (Xi’an University of Technology)

  • Bo Ming

    (Xi’an University of Technology)

  • Qiang Huang

    (Xi’an University of Technology)

  • Guoyong Leng

    (Joint Global Change Research Institute)

  • Beibei Hou

    (Xi’an University of Technology)

Abstract

It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four forecasting models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.

Suggested Citation

  • Shengzhi Huang & Bo Ming & Qiang Huang & Guoyong Leng & Beibei Hou, 2017. "A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3667-3681, September.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:11:d:10.1007_s11269-017-1692-8
    DOI: 10.1007/s11269-017-1692-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-017-1692-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-017-1692-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peiqiang Gao & Wenfeng Du & Qingwen Lei & Juezhi Li & Shuaiji Zhang & Ning Li, 2023. "NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1481-1497, March.
    2. Xiaobo Liu & Yukuan Wang & Ming Li, 2021. "How to Identify Future Priority Areas for Urban Development: An Approach of Urban Construction Land Suitability in Ecological Sensitive Areas," IJERPH, MDPI, vol. 18(8), pages 1-21, April.
    3. Xiaoyan Tang & Yongjiu Feng & Chen Gao & Zhenkun Lei & Shurui Chen & Rong Wang & Yanmin Jin & Xiaohua Tong, 2023. "Entropy-weight-based spatiotemporal drought assessment using MODIS products and Sentinel-1A images in Urumqi, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 387-408, October.
    4. Li, Huanhuan & Chen, Diyi & Arzaghi, Ehsan & Abbassi, Rouzbeh & Xu, Beibei & Patelli, Edoardo & Tolo, Silvia, 2018. "Safety assessment of hydro-generating units using experiments and grey-entropy correlation analysis," Energy, Elsevier, vol. 165(PA), pages 222-234.

    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:spr:waterr:v:31:y:2017:i:11:d:10.1007_s11269-017-1692-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.