Linear genetic programming for time-series modelling of daily flow rate
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Guven:2009:JESS,
-
author = "Aytac Guven",
-
title = "Linear genetic programming for time-series modelling
of daily flow rate",
-
journal = "Journal of Earth System Science",
-
year = "2009",
-
volume = "118",
-
number = "2",
-
pages = "137--146",
-
month = apr,
-
publisher = "Springer",
-
keywords = "genetic algorithms, genetic programming, neural
networks, daily flows, flow forecasting",
-
ISSN = "0253-4126",
-
URL = "http://www.ias.ac.in/jess/apr2009/137.pdf",
-
size = "10 pages",
-
abstract = "In this study linear genetic programming (LGP),which
is a variant of Genetic Programming,and two versions of
Neural Networks (NNs)are used in predicting time-series
of daily flow rates at a station on Schuylkill River at
Berne,PA,USA.Daily flow rate at present is being
predicted based on different time-series scenarios.For
this purpose,various LGP and NN models are calibrated
with training sets and validated by testing
sets.Additionally,the robustness of the proposed LGP
and NN models are evaluated by application data,which
are used neither in training nor at testing stage.The
results showed that both techniques predicted the flow
rate data in quite good agreement with the observed
ones,and the predictions of LGP and NN are
challenging.The performance of LGP,which was moderately
better than NN,is very promising and hence supports the
use of LGP in predicting of river flow data.",
-
notes = "Civil Engineering Department, Gaziantep University,
27310 Gaziantep, Turkey.",
- }
Genetic Programming entries for
Aytac Guven
Citations