Neural network and genetic programming for modelling coastal algal blooms

Nitin Muttil, Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

323 Citations (Scopus)


In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics. In the present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong. The study of the weights of the trained ANN and also the GP-evolved equations shows that they correctly identify the ecologically significant variables. Analysis of various ANN and GP scenarios indicates that good predictions of long-term trends in algal biomass can be obtained using only chlorophyll-a as input. The results indicate that the use of biweekly data can simulate long-term trends of algal biomass reasonably well, but it is not ideally suited to give short-term algal bloom predictions.
Original languageEnglish
Pages (from-to)223-238
Number of pages16
JournalInternational Journal of Environment and Pollution
Issue number3-4
Publication statusPublished - 18 Dec 2006


  • Algal blooms
  • Artificial neural networks
  • Genetic programming
  • Harmful algal blooms
  • Hong Kong
  • Machine learning techniques
  • Water quality modelling

ASJC Scopus subject areas

  • Waste Management and Disposal
  • Pollution
  • Management, Monitoring, Policy and Law


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