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Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data

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  • Fan, Junliang
  • Ma, Xin
  • Wu, Lifeng
  • Zhang, Fucang
  • Yu, Xiang
  • Zeng, Wenzhi

Abstract

Accurate estimation of reference evapotranspiration (ETo) is required in many fields, e.g. irrigation scheduling design, agricultural water management, crop growth modeling and drought assessment. Nevertheless, reliable estimation of ETo is difficult when lack of complete or long-term meteorological data at the target station. This study evaluated the efficiency of a new tree-based soft computing model, Light Gradient Boosting Machine (LightGBM), for estimating daily ET0 using limited local (target-station) and external (cross-station) meteorological data from 49 weather stations in humid subtropical region of China, including 16 in Jiangxi Province and other 33 in the region. The performance of LightGBM was compared with the tree-based M5 Model Tree (M5Tree) and Random Forests (RF) as well as four empirical models (Hargreaves-Samani, Tabari, Makkink and Trabert). Eight input combinations of daily meteorological data including maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (Hr), wind speed at 2 m height (U2), extraterrestrial solar radiation (Ra) and global solar radiation (Rs) calculated from sunshine duration (n) for the period 2001–2015 were used to test the models. The results showed that LightGBM was superior to M5Tree and RF in local applications under all input combinations during testing, with average root mean square error (RMSE) of 0.08–0.58 mm d−1, 0.11–0.62 mm d−1and 0.13–0.60 mm d−1, respectively. M5Tree performed slightly better than RF under input combinations 6–8, whereas RF outperformed M5Tree under the other input combinations. However, all three soft computing models produced much better daily ETo estimates than the corresponding empirical models with the same input variables. Rs was the most influential meteorological variable for daily ETo estimation in this region, followed by Tmax and Tmin, Hr and finally U2. In external applications, LightGBM also generally performed better than the RF, M5Tree and empirical models. Soft computing models developed with meteorological data from Station 57894, having the most similar climatic characteristics to the other stations, gave satisfactory ETo estimates for the 15 cross stations in Jiangxi Province, even for the other 33 stations across the humid subtropical region of China. LightGBM was proved to be efficient and exhibit good generalization capability in both local and external applications, which was thus recommended as an alternative model for daily ETo estimation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:225:y:2019:i:c:s0378377419302768
    DOI: 10.1016/j.agwat.2019.105758
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    1. Yassin, Mohamed A. & Alazba, A.A. & Mattar, Mohamed A., 2016. "Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate," Agricultural Water Management, Elsevier, vol. 163(C), pages 110-124.
    2. Landeras, Gorka & Ortiz-Barredo, Amaia & López, Jose Javier, 2008. "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)," Agricultural Water Management, Elsevier, vol. 95(5), pages 553-565, May.
    3. Fan, Junliang & Chen, Baiquan & Wu, Lifeng & Zhang, Fucang & Lu, Xianghui & Xiang, Youzhen, 2018. "Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions," Energy, Elsevier, vol. 144(C), pages 903-914.
    4. Mattar, Mohamed A., 2018. "Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt," Agricultural Water Management, Elsevier, vol. 198(C), pages 28-38.
    5. Fan, Junliang & Wu, Lifeng & Ma, Xin & Zhou, Hanmi & Zhang, Fucang, 2020. "Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions," Renewable Energy, Elsevier, vol. 145(C), pages 2034-2045.
    6. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    7. Chatzithomas, C.D. & Alexandris, S.G., 2015. "Solar radiation and relative humidity based, empirical method, to estimate hourly reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 152(C), pages 188-197.
    8. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Ma, Xin & Bai, Hua, 2019. "Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 168-186.
    9. Behrooz Keshtegar & Ozgur Kisi & Hamed Ghohani Arab & Mohammad Zounemat-Kermani, 2018. "Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1101-1116, February.
    10. Jiang, Shouzheng & Liang, Chuan & Cui, Ningbo & Zhao, Lu & Du, Taisheng & Hu, Xiaotao & Feng, Yu & Guan, Jing & Feng, Yi, 2019. "Impacts of climatic variables on reference evapotranspiration during growing season in Southwest China," Agricultural Water Management, Elsevier, vol. 216(C), pages 365-378.
    11. Talaat, M. & Farahat, M.A. & Elkholy, M.H., 2019. "Renewable power integration: Experimental and simulation study to investigate the ability of integrating wave, solar and wind energies," Energy, Elsevier, vol. 170(C), pages 668-682.
    12. Traore, Seydou & Luo, Yufeng & Fipps, Guy, 2016. "Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages," Agricultural Water Management, Elsevier, vol. 163(C), pages 363-379.
    13. Seydou Traore & Yufeng Luo & Guy Fipps, 2017. "Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4891-4908, December.
    14. Xiaohu Wen & Jianhua Si & Zhibin He & Jun Wu & Hongbo Shao & Haijiao Yu, 2015. "Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3195-3209, July.
    15. Ali Rahimikhoob & Mahmood Behbahani & Javad Fakheri, 2012. "An Evaluation of Four Reference Evapotranspiration Models in a Subtropical Climate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2867-2881, August.
    16. Tongwane, Mphethe I. & Savage, Michael J. & Tsubo, Mitsuru & Moeletsi, Mokhele E., 2017. "Seasonal variation of reference evapotranspiration and Priestley-Taylor coefficient in the eastern Free State, South Africa," Agricultural Water Management, Elsevier, vol. 187(C), pages 122-130.
    17. Shiri, Jalal, 2017. "Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran," Agricultural Water Management, Elsevier, vol. 188(C), pages 101-114.
    18. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    19. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    20. Zhang, Wenjie & Quan, Hao & Srinivasan, Dipti, 2018. "Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination," Energy, Elsevier, vol. 160(C), pages 810-819.
    21. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
    22. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    23. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 2019. "A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China," Energy, Elsevier, vol. 178(C), pages 487-507.
    24. Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
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