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

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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.

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Acknowledgements

This research was supported by the National Department Public Benefit Research Foundation of Ministry of Water Resources (201501058) and the project of School of Water Resources and Hydropower of Xi’an University of Technology (2016ZZKT-15).

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Authors and Affiliations

  1. State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, 710048, China

    Shengzhi Huang, Bo Ming, Qiang Huang & Beibei Hou

  2. Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD, 20740, USA

    Guoyong Leng

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  1. Shengzhi Huang
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  2. Bo Ming
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  3. Qiang Huang
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  4. Guoyong Leng
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  5. Beibei Hou
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Correspondence to Shengzhi Huang.

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Huang, S., Ming, B., Huang, Q. et al. A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method. Water Resour Manage 31, 3667–3681 (2017). https://doi.org/10.1007/s11269-017-1692-8

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  • DOI: https://doi.org/10.1007/s11269-017-1692-8

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