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.
Highlights
Evapotranspiration (ET) is an important component of the hydrologic cycle
The weather station corresponding to the World Meteorological Organization code 83692 and INMET code A518 is located within the Federal University of Juiz de Fora (UFJF), where an average annual rainfall of 1536 mm. has been registered for the last decades
The scope of this study was to assess how accurate Artificial Neural Networks could be at estimating reference crop evapotranspiration (ETo)
Summary
Evapotranspiration (ET) is an important component of the hydrologic cycle. Its estimation plays a central role in different fields related to hydrology such as water balance, impact of land uses assessment, water resources planning and management and irrigation system design. Evapotranspiration is the physical process where water is transferred to the atmosphere both by evaporation from land and by transpiration from plants, so it is a combined process through which moisture returns to the atmosphere. ET had always been a concept widely used as far as water resources management was concerned. In order to solve this issue, the Food and Agricultural Organization (FAO) of the United Nations and by means of the publication of the “FAO Irrigation and Drainage Paper No 56” in 1990, shed some light on the problem, helping users with uniformity on the use of such terminology
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
