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Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables

Author

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  • Tianao Wu
  • Wei Zhang
  • Xiyun Jiao
  • Weihua Guo
  • Yousef Alhaj Hamoud

Abstract

Accurate ET0 estimation is of great significance in effective agricultural water management and realizing future intelligent irrigation. This study compares the performance of five Boosting-based models, including Adaptive Boosting(ADA), Gradient Boosting Decision Tree(GBDT), Extreme Gradient Boosting(XGB), Light Gradient Boosting Decision Machine(LGB) and Gradient boosting with categorical features support(CAT), for estimating daily ET0 across 10 stations in the eastern monsoon zone of China. Six different input combinations and 10-fold cross validation method were considered for fully evaluating model accuracy and stability under the condition of limited meteorological variables input. Meanwhile, path analysis was used to analyze the effect of meteorological variables on daily ET0 and their contribution to the estimation results. The results indicated that CAT models could achieve the highest accuracy (with global average RMSE of 0.5667 mm d-1, MAE of 4199 mm d-1and Adj_R2 of 0.8514) and best stability regardless of input combination and stations. Among the inputted meteorological variables, solar radiation(Rs) offers the largest contribution (with average value of 0.7703) to the R2 value of the estimation results and its direct effect on ET0 increases (ranging 0.8654 to 0.9090) as the station’s latitude goes down, while maximum temperature (Tmax) showes the contrary trend (ranging from 0.8598 to 0.5268). These results could help to optimize and simplify the variables contained in input combinations. The comparison between models based on the number of the day in a year (J) and extraterrestrial radiation (Ra) manifested that both J and Ra could improve the modeling accuracy and the improvement increased with the station’s latitudes. However, models with J could achieve better accuracy than those with Ra. In conclusion, CAT models can be most recommended for estimating ET0 and input variable J can be promoted to improve model performance with limited meteorological variables in the eastern monsoon zone of China.

Suggested Citation

  • Tianao Wu & Wei Zhang & Xiyun Jiao & Weihua Guo & Yousef Alhaj Hamoud, 2020. "Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    • Handle: RePEc:plo:pone00:0235324
    DOI: 10.1371/journal.pone.0235324
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    References listed on IDEAS

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    7. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).
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    Cited by:

    1. Jayashree T R & NV Subba Reddy & U Dinesh Acharya, 2023. "Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1013-1032, February.
    2. Bemah Ibrahim & Isaac Ahenkorah & Anthony Ewusi, 2022. "Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    3. Kang, Yan & Chen, Peiru & Cheng, Xiao & Zhang, Shuo & Song, Songbai, 2022. "Novel hybrid machine learning framework with decomposition–transformation and identification of key modes for estimating reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 273(C).
    4. Rossy Chumbe & Stefany Silva & Yvan Garcia, 2023. "Comparison of the machine learning and AquaCrop models for quinoa crops," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 69(2), pages 65-75.

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