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Study on Real-Time Water Demand Prediction of Winter Wheat–Summer Corn Based on Convolutional Neural Network–Informer Combined Modeling

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  • Jianqin Ma

    (School of Water Conservaney, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Yijian Chen

    (School of Water Conservaney, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Xiuping Hao

    (School of Water Conservaney, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Bifeng Cui

    (School of Water Conservaney, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Jiangshan Yang

    (School of Water Conservaney, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

Abstract

The accurate prediction of crops’ water requirements is an important reference for real-time irrigation decisions on farmland. In order to achieve precise control of irrigation and improve irrigation water utilization, a real-time crop water requirement prediction model combining convolutional neural networks (CNNs) and the Informer model is presented in this paper, taking the real-time water demand of winter wheat–summer maize from 2017 to 2021 as the research object. The CNN model was used to extract the depth features of the day-by-day meteorological data of the crops, and the extracted feature values were inputted into the Informer model according to the time series for training and prediction to obtain the predicted water demand of winter wheat and summer maize. The results showed that the prediction accuracy of the constructed CNN–Informer combination model was higher compared to CNN, BP, and LSTM models, with an improvement of 1.2%, 25.1%, and 21.9% for winter wheat and 0.4%, 37.4%, and 20.3% for summer maize; based on the good performance of the model in capturing the long-term dependency relationship, the irrigation analysis using the model prediction data showed a significant water-saving effect compared with the traditional irrigation mode, with an average annual water saving of about 1004.3 m 3 /hm 2 , or 18.4%, which verified the validity of the model, and it can provide a basis for the prediction of crops’ water demand and sustainable agricultural development.

Suggested Citation

  • Jianqin Ma & Yijian Chen & Xiuping Hao & Bifeng Cui & Jiangshan Yang, 2024. "Study on Real-Time Water Demand Prediction of Winter Wheat–Summer Corn Based on Convolutional Neural Network–Informer Combined Modeling," Sustainability, MDPI, vol. 16(9), pages 1-14, April.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3699-:d:1385155
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    References listed on IDEAS

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    1. Allen, Richard G. & Pruitt, William O. & Wright, James L. & Howell, Terry A. & Ventura, Francesca & Snyder, Richard & Itenfisu, Daniel & Steduto, Pasquale & Berengena, Joaquin & Yrisarry, Javier Basel, 2006. "A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman-Monteith method," Agricultural Water Management, Elsevier, vol. 81(1-2), pages 1-22, March.
    2. Jianqin Ma & Jiangshan Yang & Xiuping Hao & Bifeng Cui & Shuoguo Yang, 2023. "Dynamic Simulation Model of Channel Leakage Based on Multiple Regression," Sustainability, MDPI, vol. 15(20), pages 1-13, October.
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