A Methodological Proposal Based on Artificial Neural Networks for Evapotranspiration Assessment

Alberto B. Mirambell, Clayton F. da Silva, Flavio de Souza Barbosa, Celso Bandeira de Melo Ribeiro

Abstract


Evapotranspiration is the combined process in which water is transferred from the soil by evaporation and through the plants by transpiration to the atmosphere. Therefore, it is a central parameter in Agriculture since it expresses the amount of water to be returned by irrigation. Aiming to standardize Evapotranspiration estimate, the term “reference crop evapotranspiration (ETo)” was coined as the rate of Evapotranspiration from a hypothetical grass surface of uniform height, actively growing, completely shading the ground and well watered. ETo can be measured with lysimeters or estimated by mathematical approaches. Although, Penman-Monteith FAO 56 (PM) is the recommended method to estimate ETo by PM, it is necessary to register maximum and minimum temperatures (ºC), solar radiation (hours), relative humidity (%) and wind speed (m/seg.). Some of these parameters are missing in the historical meteorological registers. Here, Artificial Neural Networks (ANNs) can aid traditional methodologies. ANNs learn, recognise patterns and generalise complex relationships among large datasets to produce meaningful results even when input data is wrong or incomplete. The target of this study is to assess ANNs capability to estimatie ETo values. We have built and tested several architectures guided by Levenberg-Marquardt algorithm with 5 above mentioned parameters as inputs, from 1 to 50 hidden nodes and 1 parameter as output. Architectures with 10, 15 and 20 nodes in the hidden layer brought outsanding r2 values: 0.935, 0.937, 0.937 along with the highest intercept and the lowest slope values, which demonstrate that ANNs approach was an afficient method to estimate ETo.


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DOI: https://doi.org/10.5539/jas.v9n5p142

Copyright (c) 2017 Alberto Benito

License URL: http://creativecommons.org/licenses/by/4.0

Journal of Agricultural Science   ISSN 1916-9752 (Print)   ISSN 1916-9760 (Online)  E-mail: jas@ccsenet.org

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