Linear mixed modeling and artificial neural network techniques for predicting wind drift and evaporation losses under moving sprinkler irrigation systems

2019 ◽  
Vol 38 (2) ◽  
pp. 177-188
Author(s):  
Abid Sarwar ◽  
R. Troy Peters ◽  
Abdelmoneim Zakaria Mohamed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sprinkler Irrigation ◽  
Irrigation Systems ◽  
Wind Drift ◽  
Evaporation Losses ◽  
Artificial Neural

Modeling wetted areas of moisture bulb for drip irrigation systems: An enhanced empirical model and artificial neural network

2020 ◽  
Vol 178 ◽  
pp. 105767
Author(s):  
Bakhtiar Karimi ◽  
Parva Mohammadi ◽  
Hadi Sanikhani ◽  
Sinan Q. Salih ◽  
Zaher Mundher Yaseen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Empirical Model ◽  
Drip Irrigation ◽  
Irrigation Systems ◽  
Artificial Neural

Estimation of Wind Drift and Evaporation Losses from Sprinkler Irrigation systemS by Different Data-Driven Methods

2017 ◽  
Vol 67 (2) ◽  
pp. 222-232 ◽  
Author(s):  
E. Maroufpoor ◽  
H. Sanikhani ◽  
S. Emamgholizadeh ◽  
Ö. Kişi
Keyword(s):  
Sprinkler Irrigation ◽  
Data Driven ◽  
Irrigation Systems ◽  
Wind Drift ◽  
Evaporation Losses

scholarly journals Artificial neural network model for simulation of water distribution in sprinkle irrigation

2015 ◽  
Vol 19 (9) ◽  
pp. 817-822
Author(s):  
Paulo L. de Menezes ◽  
Carlos A. V. de Azevedo ◽  
Eduardo Eyng ◽  
José Dantas Neto ◽  
Vera L. A. de Lima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Distribution ◽  
Field Trials ◽  
Operating Pressure ◽  
Ann Model ◽  
Irrigation Systems ◽  
Require Time ◽  
Artificial Neural ◽  
Sprinkle Irrigation

ABSTRACTDetermining uniformity coefficients of sprinkle irrigation systems, in general, depends on field trials, which require time and financial resources. One alternative to reduce time and expense is the use of simulations. The objective of this study was to develop an artificial neural network (ANN) to simulate sprinkler precipitation, using the values of operating pressure, wind speed, wind direction and sprinkler nozzle diameter as the input parameters. Field trials were performed with one sprinkler operating in a grid of 16 x 16, collectors with spacing of 1.5 m and different combinations of nozzles, pressures, and wind conditions. The ANN model showed good results in the simulation of precipitation, with Spearman's correlation coefficient (rs) ranging from 0.92 to 0.97 and Willmott agreement index (d) from 0.950 to 0.991, between the observed and simulated values for ten analysed trials. The ANN model shows promise in the simulation of precipitation in sprinkle irrigation systems.

Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques

2018 ◽  
Vol 195 ◽  
pp. 211-221 ◽  
Author(s):  
Hussein M. Al-Ghobari ◽  
Mohamed S. El-Marazky ◽  
Ahmed Z. Dewidar ◽  
Mohamed A. Mattar
Keyword(s):  
Neural Network ◽  
Multiple Regression ◽  
Sprinkler Irrigation ◽  
Wind Drift ◽  
Evaporation Losses ◽  
Regression Techniques

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

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

2019 ◽  
Author(s):  
Johannes Thüring ◽  
Kevin Linka ◽  
Christiane Kuhl ◽  
Sven Nebelung ◽  
Daniel Truhn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Mri ◽  
Artificial Neural
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