Can we obtain viable alternatives to Manning's equation using genetic programming?

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{journals/aires/GaitanBM16,
  author =       "Carlos F. Gaitan and Venkatramani Balaji and 
                 Berrien {Moore III}",
  title =        "Can we obtain viable alternatives to {Manning's}
                 equation using genetic programming?",
  journal =      "Artificial Intelligence Research",
  year =         "2016",
  number =       "2",
  volume =       "5",
  pages =        "92--101",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1927-6974",
  bibdate =      "2017-05-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.5430/air.v5n2p92;
                 DBLP,
                 http://dblp.uni-trier.de/db/journals/aires/aires5.html#GaitanBM16",
  DOI =          "doi:10.5430/air.v5n2p92",
  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