Winter diatom blooms in a regulated river in South Korea: explanations based on evolutionary computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4472

@Article{Kim:2007:FWB,
  author =       "Dong-Kyun Kim and Kwang-Seuk Jeong and 
                 Peter A. Whigham and Gea-Jae Joo",
  title =        "Winter diatom blooms in a regulated river in South
                 Korea: explanations based on evolutionary computation",
  journal =      "Freshwater Biology",
  year =         "2007",
  volume =       "52",
  pages =        "2021--2041",
  keywords =     "genetic algorithms, genetic programming, diatom bloom
                 mechanism, ecological modelling, sensitivity analysis,
                 stephanodiscus hantzchii",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.717.6431",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.717.6431",
  URL =          "http://infosci.otago.ac.nz/assets/staff/pwhigham/publications/fwb1804.pdf",
  DOI =          "doi:10.1111/j.1365-2427.2007.01804.x",
  size =         "21 pages",
  abstract =     "1. An ecological model was developed using genetic
                 programming (GP) to predict the time-series dynamics of
                 the diatom, Stephanodiscus hantzschii for the lower
                 Nakdong River, South Korea. Eight years of weekly data
                 showed the river to be hypertrophic (chl. a, 45.1 pm
                 4.19 lg L )1 , mean pm SE, n 1a--4 427), and S.
                 hantzschii annually formed blooms during the winter to
                 spring flow period (late November to March).

                 2. A simple non-linear equation was created to produce
                 a 3-day sequential forecast of the species biovolume,
                 by means of time series optimisation genetic
                 programming (TSOGP). Training data were used in
                 conjunction with a GP algorithm using 7 years of
                 limnological variables (1995-2001). The model was
                 validated by comparing its output with measurements for
                 a specific year with severe blooms (1994). The model
                 accurately predicted timing of the blooms although it
                 slightly underestimated biovolume (training r 2 1a--4
                 0.70, test r 2 1a--4 0.78). The model consisted of the
                 following variables: dam discharge and storage, water
                 temperature, Secchi transparency, dissolved oxygen
                 (DO), pH, evaporation and silica concentration.

                 3. The application of a five-way cross-validation test
                 suggested that GP was capable of developing models
                 whose input variables were similar, although the data
                 are randomly used for training. The similarity of input
                 variable selection was approximately 51percent between
                 the best model and the top 20 candidate models out of
                 150 in total (based on both Root Mean Squared Error and
                 the determination coefficients for the test data).

                 4. Genetic programming was able to determine the
                 ecological importance of different environmental
                 variables affecting the diatoms. A series of
                 sensitivity analyses showed that water temperature was
                 the most sensitive parameter. In addition, the optimal
                 equation was sensitive to DO, Secchi transparency, dam
                 discharge and silica concentration. The analyses thus
                 identified likely causes of the proliferation of
                 diatoms in `river-reservoir hybrids' (i.e. rivers which
                 have the characteristics of a reservoir during the dry
                 season). This result provides specific information
                 about the bloom of S. hantzschii in river systems, as
                 well as the applicability of inductive methods, such as
                 evolutionary computation to river-reservoir hybrid
                 systems.",
  notes =        "Department of Biology, Pusan National University,
                 Jang-Jeon Dong, Gum-Jeong Gu, Busan, Korea

                 Department of Information Science, University of Otago,
                 Dunedin, New Zealand",
}

Genetic Programming entries for Dong-Kyun Kim Kwang-Seuk Jeong Peter Alexander Whigham Gea-Jae Joo

Citations