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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
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    Citations

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    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. 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.
    3. 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.
    4. 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.

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