Abstract
Reference crop evapotranspiration (ETO) is a basic component of the hydrological cycle and its estimation is critical for agricultural water resource management and scheduling. In this study, three tree-based machine learning algorithms (random forest [RF], gradient boosting decision tree [GBDT], and extreme gradient boosting [XGBoost]) were adopted to determine the essential factors for ETO prediction. The tree-based models were optimized using the Bayesian optimization (BO) algorithm and were compared with three standalone models in terms of daily ETO and monthly mean ETO estimations in North China, with different input combinations of essential variables. The results indicated that solar radiation (Rs) and air temperature (Ts), including the maximum, minimum, and average temperatures, in daily ETO were the key variables affecting model prediction accuracy. Rs was the most influential factor in the monthly average ETO model followed by Ts. Both relative humidity (RH) and wind speed at 2 m (U2) had little impact on ETO prediction at different scales, although their importance differed. Compared with the GBDT and RF models, the XGBoost model exhibited the best performance for daily ETO and monthly mean ETO estimations. The hybrid tree-based models with the BO algorithm outperformed standalone tree-based models. Overall, compared with the other inputs, the model with three inputs (Rs, Ts, and RH/U2) had the highest accuracy. The BO-XGBoost model exhibited superior performance in terms of the global performance index (GPI) for daily ETO and monthly mean ETO predictions and is recommended as a more accurate model for predicting daily ETO and monthly mean ETO in North China or areas with a similar climate.
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Data availability
The datasets generated during and analysed during the current study are available from the corresponding author on 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 also supported by National Natural Science Foundation of China (52309050, 51922072, 51779161, 51009101), Key R&D and Promotion Projects in Henan Province (Science and Technology Development) (No. 232102110264 & 202102110112), the Fundamental Research Funds for the Central Universities (2019CDLZ-10, 2020CDDZ-19), Experimental Technology Development Fund Project of Henan University of Science and Technology(No. SY2021008), PhD Research Startup Foundation of Henan University of Science and Technology (No. 13480025), 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).
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Conceptualization: Long Zhao, Ningbo Cui; Methodology: Long Zhao, Yuhang Wang, Yi Shi, Xinbo Zhao; Formal analysis and investigation: Long Zhao, Yuhang Wang, Yi Shi, Xinbo Zhao, Shuo Zhang; Writing—original draft preparation: Long Zhao, Yuhang Wang; Writing—review and editing: Ningbo Cui; Funding acquisition: Long Zhao, Ningbo Cui; Supervision: Ningbo Cui. All authors have read and agreed to the published version of the manuscript.
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Zhao, L., Wang, Y., Shi, Y. et al. Selecting essential factors for predicting reference crop evapotranspiration through tree-based machine learning and Bayesian optimization. Theor Appl Climatol 155, 2953–2972 (2024). https://doi.org/10.1007/s00704-023-04760-2
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DOI: https://doi.org/10.1007/s00704-023-04760-2