Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application
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- @Article{Elshorbagy:2010a:HESS,
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title = "Experimental investigation of the predictive
capabilities of data driven modeling techniques in
hydrology - Part 2: Application",
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author = "A. Elshorbagy and G. Corzo and S. Srinivasulu and
D. P. Solomatine",
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journal = "Hydrology and Earth System Sciences",
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year = "2010",
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volume = "14",
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number = "10",
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pages = "1943--1961",
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month = "14 " # oct,
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keywords = "genetic algorithms, genetic programming",
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ISSN = "10275606",
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bibsource = "OAI-PMH server at www.doaj.org",
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language = "eng",
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oai = "oai:doaj-articles:0b5621edb6cf47d7aee8cedce805592b",
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source = "Hydrology and Earth System Sciences",
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URL = "http://www.hydrol-earth-syst-sci.net/14/1943/2010/hess-14-1943-2010.pdf",
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size = "19 pages",
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abstract = "In this second part of the two-part paper, the data
driven modeling (DDM) experiment, presented and
explained in the first part, is implemented. Inputs for
the five case studies (half-hourly actual
evapotranspiration, daily peat soil moisture, daily
till soil moisture, and two daily rainfall-runoff
datasets) are identified, either based on previous
studies or using the mutual information content. Twelve
groups (realisations) were randomly generated from each
data set by randomly sampling without replacement from
the original data set. Neural networks (ANNs), genetic
programming (GP), evolutionary polynomial regression
(EPR), Support vector machines (SVM), M5 model trees
(M5), K-nearest neighbors (K-nn), and multiple linear
regression (MLR) techniques are implemented and applied
to each of the 12 realizations of each case study. The
predictive accuracy and uncertainties of the various
techniques are assessed using multiple average overall
error measures, scatter plots, frequency distribution
of model residuals, and the deterioration rate of
prediction performance during the testing phase. Gamma
test is used as a guide to assist in selecting the
appropriate modeling technique. Unlike two nonlinear
soil moisture case studies, the results of the
experiment conducted in this research study show that
ANNs were a sub-optimal choice for the actual
evapotranspiration and the two rainfall-runoff case
studies. GP is the most successful technique due to its
ability to adapt the model complexity to the model ed
data. EPR performance could be close to GP with
datasets that are more linear than nonlinear. SVM is
sensitive to the kernel choice and if appropriately
selected, the performance of SVM can improve. M5
performs very well with linear and semi linear data,
which cover wide range of hydrological situations. In
highly nonlinear case studies, ANNs, K-nn, and GP could
be more successful than other modelling techniques.
K-nn is also successful in linear situations, and it
should not be ignored as a potential modelling
technique for hydrological applications.",
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notes = "See also \cite{Elshorbagy:2010:HESS}",
- }
Genetic Programming entries for
Amin Elshorbagy
Gerald Corzo Perez
Sanaga Srinivasulu
Dimitri P Solomatine
Citations