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Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data

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Abstract

Crop evapotranspiration is a key parameter influencing water-saving irrigation and water resources management of agriculture. However, current models for estimating maize evapotranspiration primarily rely on meteorological data and empirical coefficients, and the estimated evapotranspiration contains uncertainties. In this study, the evapotranspiration data of summer maize were collected from typical stations in Northern China (Yucheng Station), and a back-propagation neural network (BP) model for predicting maize evapotranspiration was constructed based on meteorological data, soil data, and crop data. To further improve its accuracy, the maize evapotranspiration model was optimized using three bionic optimization algorithms, namely the sand cat swarm optimization (SCSO) algorithms, hunter–prey optimizer (HPO) algorithm, and golden jackal optimization (GJO) algorithm. The results showed that the fusion of meteorological, soil moisture, and crop data can effectively improve the accuracy of the maize evapotranspiration model. The model showed higher accuracy with the hybrid optimization model SCSO-BP compared to the stand-alone BP neural network model, with improvements of 2.7–4.8%, 17.2–25.5%, 13.9–26.8%, and 3.3–5.6% in terms of R2, RMSE, MAE, and NSE, respectively. Comprehensively compared with existing maize evapotranspiration models, the SCSO-BP model presented the highest accuracy, with R2 = 0.842, RMSE = 0.433 mm/day, MAE = 0.316 mm/day, NSE = 0.840, and overall global evaluation index (GPI) ranking the first. The results have reference value for the calculation of daily evapotranspiration of maize in similar areas of northern China.

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Acknowledgements

We would like to thank the National Ecosystem Science Data Center, National Science & Technology Infrastructure of China for providing the climate database used in this study.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 52309050 & 51809217), Key R&D and Promotion Projects in Henan Province (Science and Technology Development) (No. 232102110264 & 222102110452 & 212102110035), PhD Research Startup Foundation of Henan University of Science and Technology (No. 13480025 & 13480033), Henan Provincial Tobacco Company Luoyang City Company Technology Innovation Pro (No. 2023410300200043), and Key Scientific Research Projects of Colleges and Universities in Henan Province (No. 24B416001 & 22B416002).

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Authors and Affiliations

  1. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China

    Long Zhao, Shunhao Qing, Hui Li, Zhaomei Qiu, Xiaoli Niu & Yi Shi

  2. College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China

    Shuangchen Chen

  3. Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling, Xianyang, 712100, Shaanxi Province, China

    Xuguang Xing

Authors
  1. Long Zhao
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  2. Shunhao Qing
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  3. Hui Li
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  4. Zhaomei Qiu
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  5. Xiaoli Niu
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  6. Yi Shi
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  8. Xuguang Xing
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Contributions

LZ: conceptualization, methodology, supervision, funding acquisition. SQ: writing—original draft, formal analysis, software. HL: investigation, data curation, software. ZQ: formal analysis. XN: visualization, funding acquisition. YS: investigation. SC: visualization. XX: software, writing—review and editing

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Correspondence to Xuguang Xing.

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Zhao, L., Qing, S., Li, H. et al. Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data. Int J Biometeorol 68, 511–525 (2024). https://doi.org/10.1007/s00484-023-02608-y

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