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Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China

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Abstract

The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399–0.554. RH and Ra are also key factors influencing ETO; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408–1.964, R2 of 0.545–0.982, mean absolute error of 0.273–1.086, and Nash–Sutcliffe efficiency coefficient of 0.658–0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.

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Data availability

Data supporting this study’s findings are available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to thank the National Climatic Centre of the China Meteorological Administration for providing the climate database used in this study.

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 51909079 and 51809217), Key R&D and Promotion Projects in Henan Province (Science and Technology Development) (Nos. 202102110112, 222102110452, and 222102110360), PhD Research Startup Foundation of Henan University of Science and Technology (Nos. 13480025 and 13480033), and Key Scientific Research Projects of Colleges and Universities in Henan Province (No. 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, Xinbo Zhao, Yuanze Li, Yi Shi & Hanmi Zhou

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

    Xiuzhen Li

  3. College of Agriculture, Henan University of Science and Technology, Henan Province, Luoyang, 471000, China

    Xiaodong Wang

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

    Xuguang Xing

Authors
  1. Long Zhao
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  2. Xinbo Zhao
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  3. Yuanze Li
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Contributions

Long Zhao: conceptualization, methodology, supervision, funding acquisition. Xinbo Zhao: writing—original draft, formal analysis, software. Yuanze Li: investigation, data curation, software. Yi Shi: formal analysis. Hanmi Zhou: visualization, funding acquisition. Xiuzhen Li: investigation. Xiaodong Wang: software. Xuguang Xing: writing—review and editing.

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Correspondence to Xuguang Xing.

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Zhao, L., Zhao, X., Li, Y. et al. Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China. Environ Sci Pollut Res 30, 22396–22412 (2023). https://doi.org/10.1007/s11356-022-23786-z

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