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. Modelling of evapotranspiration using artificial neural network.

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Title . Modelling of evapotranspiration using artificial neural network.
. Modelling of evapotranspiration using artificial neural network.
 
Creator ICAR_CRIDA
 
Subject This study investigates the utility of artificial neural networks ~ANNs! for estimation of daily grass reference crop evapotranspiration ~ETo! and compares the performance of ANNs with the conventional method ~Penman–Monteith! used to estimate ETo. Several issues associated with the use of ANNs are examined, including different learning methods, number of processing elements in the hidden layer~s!, and the number of hidden layers. Three learning methods, namely, the standard back-propagation with learning rates of 0.2 and 0.8, and backpropagation with momentum were considered. The best ANN architecture for estimation of daily ETo was obtained for two different data sets ~Sets 1 and 2! for Davis, Calif. Using data of Set 1, the networks were trained with daily climatic data ~solar radiation, maximum and minimum temperature, maximum and minimum relative humidity, and wind speed! as input and the Penman– Monteith ~PM! estimated ETo as output. The best ANN architecture was selected on the basis of weighted standard error of estimate ~WSEE! and minimal ANN architecture. The ANN architecture of 6-7-1, ~six, seven, and one neuron~s! in the input, hidden, and output layers, respectively! gave the minimum WSEE ~less than 0.3 mm/day! for all learning methods. This value was lower than the WSEE ~0.74 mm/day! between the PM method and lysimeter measured ETo as reported by Jensen et al. in 1990. Similarly, ANNs were trained, validated, and tested using the lysimeter measured ETo and corresponding climatic data ~Set 2!. Again, all learning methods gave less WSEE ~less than 0.60 mm/day! as compared to the PM method ~0.97 mm/day!. Based on these results, it can be concluded that the ANN can predict ETo better than the conventional method ~PM! for Davis.
 
Description Not Available
This study investigates the utility of artificial neural networks ~ANNs! for estimation of daily grass reference crop evapotranspiration ~ETo! and compares the performance of ANNs with the conventional method ~Penman–Monteith! used to estimate ETo.
Several issues associated with the use of ANNs are examined, including different learning methods, number of processing elements in the
hidden layer~s!, and the number of hidden layers. Three learning methods, namely, the standard back-propagation with learning rates of
0.2 and 0.8, and backpropagation with momentum were considered. The best ANN architecture for estimation of daily ETo was obtained
for two different data sets ~Sets 1 and 2! for Davis, Calif. Using data of Set 1, the networks were trained with daily climatic data ~solar
radiation, maximum and minimum temperature, maximum and minimum relative humidity, and wind speed! as input and the Penman–
Monteith ~PM! estimated ETo as output. The best ANN architecture was selected on the basis of weighted standard error of estimate
~WSEE! and minimal ANN architecture. The ANN architecture of 6-7-1, ~six, seven, and one neuron~s! in the input, hidden, and output
layers, respectively! gave the minimum WSEE ~less than 0.3 mm/day! for all learning methods. This value was lower than the WSEE
~0.74 mm/day! between the PM method and lysimeter measured ETo as reported by Jensen et al. in 1990. Similarly, ANNs were trained,
validated, and tested using the lysimeter measured ETo and corresponding climatic data ~Set 2!. Again, all learning methods gave less
WSEE ~less than 0.60 mm/day! as compared to the PM method ~0.97 mm/day!. Based on these results, it can be concluded that the ANN
can predict ETo better than the conventional method ~PM! for Davis.
Not Available
 
Date 2020-02-25T08:19:03Z
2020-02-25T08:19:03Z
2011
 
Type Technical Report
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/32744
 
Language English
 
Relation Not Available;
 
Publisher Kumar M, Bandyopadhyay A, Raghuwanshi NS