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Prediction of agricultural grey water footprint in Henan Province based on GM(1,N)-BP neural network

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Abstract

The GM(1,N) model can consider the impact of many factors on a system, but it is only suitable for short-term prediction of small samples, and its ability to mine data information is weak. In response to this problem, the BP neural network is introduced, and the powerful function approximation ability of neural networks is used to approximate the GM(1,N) model. A new GM(1,N)-BP neural network model is proposed to predict the agricultural grey water footprint in Henan Province. On this basis, the future development trend of agricultural grey water footprint is analyzed. The results show that, after comparison and verification, the new GM(1,N)-BP neural network combination model can greatly improve the prediction accuracy of grey water footprint. From 2021 to 2025, the model predicts that the agricultural grey water footprint of Henan Province will continue to rise, with an increase of 17% compared with 2020. Spatially, the grey water footprint increases from north to south, and in the east–west direction, it is high at both ends and low in the middle, but it is higher in the west than in the east.

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Funding

This work is supported by the Key R&D and Promotion Projects of Henan Province (Soft Science) (222400410352).

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Authors and Affiliations

  1. School of Information and Management Science, Henan Agricultural University, Zhengzhou, 450000, Henan, China

    Wenyan Li, Bingjun Li & Wenya Ma

Authors
  1. Wenyan Li
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  2. Bingjun Li
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  3. Wenya Ma
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Contributions

WL was responsible for data processing and writing of the first draft of the manuscript. WM was responsible for for drawing pictures and revising the paper. BL was responsible for proposing the overall idea and framework of the manuscript.

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Correspondence to Wenya Ma.

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The authors have no conflicts of interest.

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Handling Editor: Luiz Duczmal.

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Li, W., Li, B. & Ma, W. Prediction of agricultural grey water footprint in Henan Province based on GM(1,N)-BP neural network. Environ Ecol Stat 30, 335–354 (2023). https://doi.org/10.1007/s10651-023-00559-6

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  • DOI: https://doi.org/10.1007/s10651-023-00559-6

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