- 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",
- broken = "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",
