Prediction of Algal Bloom Using Genetic Programming


Sivapragasam, C, Muttil, Nitin ORCID: 0000-0001-7758-8365, Muthukumar, S and Arun, VM (2010) Prediction of Algal Bloom Using Genetic Programming. Marine Pollution Bulletin, 60 (10). pp. 1849-1855. ISSN 0025-326X


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.

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Item type Article
Official URL
Subjects Historical > RFCD Classification > 290000 Engineering and Technology
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > RFCD Classification > 280000 Information, Computing and Communication Sciences
Keywords ResPubID19498, genetic programming, mathematical modeling, harmful algal bloom
Citations in Scopus 45 - View on Scopus
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