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Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions

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  • Xiaohu Wen
  • Jianhua Si
  • Zhibin He
  • Jun Wu
  • Hongbo Shao
  • Haijiao Yu

Abstract

Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET 0 ) using limited climatic data. For the SVM, four combinations of maximum air temperature (T max ), minimum air temperature (T min ), wind speed (U 2 ) and daily solar radiation (R s ) in the extremely arid region of Ejina basin, China, were used as inputs with T max and T min as the base data set. The results of SVM models were evaluated by comparing the output with the ET 0 calculated using Penman–Monteith FAO 56 equation (PMF-56). We found that the ET 0 estimated using SVM with limited climatic data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological data. In particular, three climatic parameters, T max , T min , and R s were enough to predict the daily ET 0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET 0 with scarce data in extreme arid regions. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:9:p:3195-3209
    DOI: 10.1007/s11269-015-0990-2
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    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.
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    14. 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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