Evapotranspiration evaluation models based on machine learning algorithms—A comparative study

Francesco Granata

Agricultural Water Management, 2019, vol. 217, issue C, 303-315

Abstract: The constant need to increase agricultural production, together with the more and more frequent drought events in many areas of the world, requires a more careful assessment of irrigation needs and, therefore, a more accurate estimation of actual evapotranspiration. In recent years, several water management issues have been addressed by means of models derived from Artificial Intelligence research. When using machine learning based models, the main challenging aspects are represented by the choice of the best possible algorithm, the choice of adequately representative variables and the availability of appropriate data sets.

Keywords: Actual evapotranspiration; Machine learning; Regression tree; Ensemble methods; Support vector regression; Irrigation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:217:y:2019:i:c:p:303-315

DOI: 10.1016/j.agwat.2019.03.015

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