Prediction and simulation of monthly groundwater levels by genetic programming
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- @Article{FallahMehdipour:2013:JHR,
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author = "E. Fallah-Mehdipour and O. {Bozorg Haddad} and
M. A. Marino",
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title = "Prediction and simulation of monthly groundwater
levels by genetic programming",
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journal = "Journal of Hydro-environment Research",
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year = "2013",
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volume = "7",
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number = "4",
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pages = "253--260",
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keywords = "genetic algorithms, genetic programming, Adaptive
neural fuzzy inference system, Prediction, Simulation,
Groundwater level",
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ISSN = "1570-6443",
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DOI = "doi:10.1016/j.jher.2013.03.005",
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URL = "http://www.sciencedirect.com/science/article/pii/S1570644313000270",
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abstract = "Groundwater level is an effective parameter in the
determination of accuracy in groundwater modelling.
Thus, application of simple tools to predict future
groundwater levels and fill-in gaps in data sets are
important issues in groundwater hydrology. Prediction
and simulation are two approaches that use previous and
previous-current data sets to complete time series.
Artificial intelligence is a computing method that is
capable to predict and simulate different system states
without using complex relations. This paper
investigates the capability of an adaptive neural fuzzy
inference system (ANFIS) and genetic programming (GP)
as two artificial intelligence tools to predict and
simulate groundwater levels in three observation wells
in the Karaj plain of Iran. Precipitation and
evaporation from a surface water body and water levels
in observation wells penetrating an aquifer system are
used to fill-in gaps in data sets and estimate monthly
groundwater level series. Results show that GP
decreases the average value of root mean squared error
(RMSE) as the error criterion for the observation wells
in the training and testing data sets 8.35 and 11.33
percent, respectively, compared to the average of RMSE
by ANFIS in prediction. Similarly, the average value of
RMSE for different observation wells used in simulation
improves the accuracy of prediction 9.89 and 8.40
percent in the training and testing data sets,
respectively. These results indicate that the proposed
prediction and simulation approach, based on GP, is an
effective tool in determining groundwater levels.",
- }
Genetic Programming entries for
Elahe Fallah-Mehdipour
Omid Bozorg Haddad
Miguel A Marino
Citations