Can we obtain viable alternatives to Manning's equation using genetic programming?
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- @Article{journals/aires/GaitanBM16,
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author = "Carlos F. Gaitan and Venkatramani Balaji and
Berrien {Moore III}",
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title = "Can we obtain viable alternatives to {Manning's}
equation using genetic programming?",
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journal = "Artificial Intelligence Research",
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year = "2016",
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number = "2",
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volume = "5",
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pages = "92--101",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1927-6974",
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bibdate = "2017-05-18",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/aires/aires5.html#GaitanBM16",
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DOI = "doi:10.5430/air.v5n2p92",
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abstract = "Applied water research, like the one derived from
open-channel hydraulics, traditionally links empirical
formulas to observational data; for example Manning's
formula for open channel flow driven by gravity relates
the discharge (Q), cross-sectional average velocity
(V), the hydraulic radius (R), and the slope of the
water surface (S) with a friction coefficient n,
characteristic of the channel's surface needed in the
location of interest. Here we use Genetic Programming
(GP), a machine learning technique inspired by nature's
evolutionary rules, to derive empirical relationships
based on synthetic datasets of the aforementioned
parameters. Specifically, we evaluated if Manning's
formula could be retrieved from datasets with: a) 300
pentads of A, n, R, S, and Q (from Manning's equation),
b) from datasets containing an uncorrelated variable
and the parameters from (a), and c) from a dataset
containing the parameters from (b) but using values of
Q containing noise. The cross-validated results show
success retrieving the functional form from the
synthetic data in the first two experiments, and a more
complex solution of Q for the third experiment. The
results encourage the application of GP on problems
where traditional empirical relationships show high
biases or are non-parsimonious. The results also show
alternative flow equations that might be used in the
absence of one or more predictors; however, these
equations should be used with caution outside of the
training intervals.",
- }
Genetic Programming entries for
Carlos F Gaitan
Venkatramani Balaji
Berrien Moore III
Citations