Chapter 2 - Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression
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- @InCollection{ALI:2020:HPM,
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author = "Mumtaz Ali and Ravinesh C. Deo",
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title = "Chapter 2 - Modeling wheat yield with data-intelligent
algorithms: artificial neural network versus genetic
programming and minimax probability machine
regression",
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editor = "Pijush Samui and Dieu {Tien Bui} and
Subrata Chakraborty and Ravinesh C. Deo",
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booktitle = "Handbook of Probabilistic Models",
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publisher = "Butterworth-Heinemann",
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pages = "37--87",
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year = "2020",
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isbn13 = "978-0-12-816514-0",
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DOI = "doi:10.1016/B978-0-12-816514-0.00002-3",
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URL = "http://www.sciencedirect.com/science/article/pii/B9780128165140000023",
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keywords = "genetic algorithms, genetic programming, Agricultural
precision, Artificial neural network, Minimax
probability machine regression, Wheat yield model",
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abstract = "In precision agriculture, data-intelligent algorithms
applied for predicting wheat yield can generate crucial
information about enhancing crop production and
strategic decision-making. In this chapter, artificial
neural network (ANN) model is trained with three
neighboring station-based wheat yields to predict the
yield for two nearby objective stations that share a
common geographic boundary in the agricultural belt of
Pakistan. A total of 2700 ANN models (with a
combination of hidden neurons, training algorithm, and
hidden transfer/output functions) are developed by
trial-and-error method, attaining the lowest mean
square error, in which the 90 best-ranked models for
3-layered neuronal network are used for wheat
prediction. Models such as learning algorithms
comprised of pure linear, tangent, and logarithmic
sigmoid equations in hidden transfer/output functions,
executed by Levenberg-Marquardt, scaled conjugate
gradient, conjugate gradient with Powell-Beale
restarts, Broyden-Fletcher-Goldfarb-Shanno
quasi-Newton, Fletcher-Reeves update, one-step secant,
conjugate gradient with Polak-Ribiere updates, gradient
descent with adaptive learning, gradient descent with
momentum, and gradient descent with momentum adaptive
learning method are trained. For the predicted wheat
yield at objective station 1 (i.e., Toba Taik Singh),
the optimal architecture was 3-14-1
(input-hidden-output neurons) trained with the
Levenberg-Marquardt algorithm and logarithmic sigmoid
as activation and tangent sigmoid as output function,
while at objective station 2 (i.e., Bakkar), the
Levenberg-Marquardt algorithm provided the best
architecture (3-20-1) with pure liner as activation and
tangent sigmoid as output function. The results are
benchmarked with those from minimax probability machine
regression (MPMR) and genetic programming (GP) in
accordance with statistical analysis of predicted yield
based on correlations (r), Willmott's index (WI),
Nash-Sutcliffe coefficient (EV), root mean-squared
error (RMSE), and mean absolute error (MAE). For
objective station 1, the ANN model attained the r value
of approximately 0.983, with WIapprox0.984 and
EVapprox0.962, while the MPMR model attained
rapprox0.957, WIapprox0.544, and EVapprox0.527, with
the results attained by GP model, rapprox0.982,
WIapprox0.980, and EVapprox0.955. For optimal ANN
model, a relatively low value of RMSE approx
192.02kg/ha and MAE approx 162.75kg/ha was registered
compared with the MPMR (RMSE approx 614.46kg/ha; MAE
approx 431.29kg/ha) and GP model (RMSE approx
209.25kg/ha; MAE approx 182.84kg/ha). For both
objective stations, ANN was found to be superior, as
confirmed by a larger Legates-McCabe's (LM) index used
in conjunction with relative RMSE and MAE. Accordingly,
it is averred that ANN is considered as a useful
data-intelligent contrivance for predicting wheat yield
by using nearest neighbor yield",
- }
Genetic Programming entries for
Mumtaz Ali
Ravinesh C Deo
Citations