Prediction of algal blooms using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "C. Sivapragasam and Nitin Muttil and S. Muthukumar and 
                 V. M. Arun",
  title =        "Prediction of algal blooms using genetic programming",
  journal =      "Marine Pollution Bulletin",
  volume =       "60",
  number =       "10",
  pages =        "1849--1855",
  year =         "2010",
  ISSN =         "0025-326X",
  DOI =          "doi:10.1016/j.marpolbul.2010.05.020",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Mathematical
                 modelling, Harmful algal bloom",
  abstract =     "In this study, an attempt was made to mathematically
                 model and predict algal blooms in Tolo Harbor (Hong
                 Kong) using genetic programming (GP). Chlorophyll plays
                 a vital role in blooms and was used in this model as a
                 measure of algal bloom biomass, and eight other
                 variables were used as input for its prediction. It has
                 been observed that GP evolves multiple models with
                 almost the same values of errors-of-measure. Previous
                 studies on GP modeling have primarily focused on
                 comparing GP results with actual values. In contrast,
                 in this study, the main aim was to propose a systematic
                 procedure for identifying the most appropriate GP model
                 from a list of feasible models (with similar
                 error-of-measure) using a physical understanding of the
                 process aided by data interpretation. Evaluation of the
                 GP-evolved equations shows that they correctly identify
                 the ecologically significant variables. Analysis of the
                 final GP-evolved mathematical model indicates that, of
                 the eight variables assumed to affect algal blooms, the
                 most significant effects are due to chlorophyll, total
                 inorganic nitrogen and dissolved oxygen for a 1-week
                 prediction. For longer lead predictions (biweekly),
                 secchi-disc depth and temperature appear to be
                 significant variables, in addition to chlorophyll.",

Genetic Programming entries for C Sivapragasam Nitin Muttil S Muthukumar V M Arun