Application of artificial neural network for reference crop evapotranspiration estimation

2004 ◽  
pp. 2011-2016
Author(s):  
V Jothiprakash ◽  
M Devamane
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crop Evapotranspiration ◽  
Reference Crop Evapotranspiration ◽  
Artificial Neural ◽  
Evapotranspiration Estimation ◽  
Reference Crop

scholarly journals A Methodological Proposal Based on Artificial Neural Networks for Evapotranspiration Assessment

2017 ◽  
Vol 9 (5) ◽  
pp. 142
Author(s):  
Alberto B. Mirambell ◽  
Clayton F. da Silva ◽  
Flavio De Souza Barbosa ◽  
Celso Bandeira de Melo Ribeiro
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Evapotranspiration ◽  
Reference Crop Evapotranspiration ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Complex Relationships ◽  
Hidden Layer ◽  
Reference Crop ◽  
Hidden Nodes

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.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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