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

Shengzhi Huang (), Bo Ming, Qiang Huang, Guoyong Leng and Beibei Hou
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 11, No 24, 3667-3681

Abstract: 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.

Keywords: Combination forecasting model; NDVI; The entropy weight method; SVM; The Yellow River basin (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11269-017-1692-8

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