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Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland

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  • Chen, Han
  • Huang, Jinhui Jeanne
  • McBean, Edward

Abstract

Prediction of vegetation transpiration (T) is of increasing importance in water resources management and agricultural practices, in particular to facilitate precision irrigation. Traditional evapotranspiration (ET) partitioning dual source modeling requires an extensive array of ground-level parameters and needs model correction and calibration to attain model certainty. In response, a quick and low-cost method is described to predict T using artificial intelligence (AI) modeling based on meteorological factors, status of crop growth factors and soil parameters. This study compares Random Forest (RF) and Support Vector Regression (SVR) in building AI models using three years (2014–2017) of continuous high-resolution monitoring data in a cabbage farmland. Input data included air temperature (Ta), solar radiation (Ra), relative humidity (RH), vapor pressure deficit (VPD), wind speed (Ws), soil moisture (SM), vegetation height (H), and leaf area index (LAI). The results show that soil surface resistance calculations by Monte Carlo iterative method and vegetation stomatal resistance calculations and carbon dioxide concentration and emission, improve performance of the original Shuttleworth–Wallace(S-W) model. In addition, the AI model indicates Ta and Ra are essential inputs for both model types. When there are sufficient observation data, or only lacking soil and vegetation data, the RF model is recommended for use. When there are only limited data or lack of critical Ta and Ra data, the SVR model is the preferred model. Scientific guidance is provided for agriculture precision irrigation, indicating which AI model can best estimate T and water demand for irrigation planning and water management.

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  • Chen, Han & Huang, Jinhui Jeanne & McBean, Edward, 2020. "Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland," Agricultural Water Management, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:agiwat:v:228:y:2020:i:c:s0378377419313034
    DOI: 10.1016/j.agwat.2019.105923
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    5. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    6. Wan, Wei & Han, Yiwen & Wu, Hanqing & Liu, Fan & Liu, Zhong, 2021. "Application of the source–sink landscape method in the evaluation of agricultural non-point source pollution: First estimation of an orchard-dominated area in China," Agricultural Water Management, Elsevier, vol. 252(C).
    7. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    8. Fan, Junliang & Zheng, Jing & Wu, Lifeng & Zhang, Fucang, 2021. "Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models," Agricultural Water Management, Elsevier, vol. 245(C).

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