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

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  • Granata, Francesco

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.

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  • Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    • Handle: RePEc:eee:agiwat:v:217:y:2019:i:c:p:303-315
    DOI: 10.1016/j.agwat.2019.03.015
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    References listed on IDEAS

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