Analysis of short-term estuarine phytoplankton dynamics using neural networks

Ying Zhang, Xie Zhenhua Xie, D. Joshua Parris, Risa A. Cohen

Research output: Contribution to book or proceedingConference articlepeer-review

1 Scopus citations

Abstract

The artificial neural network (ANN) approach was investigated to model the short-term phytoplankton dynamics in the Skidaway River Estuary. The ability of ANN to model phytoplankton biomass and density of dominant species was evaluated using surface water sampling data collected during bloom and non-bloom periods. During the spring bloom period, the ANN models provided good accuracy for phytoplankton biomass and densities of rapidly growing species using salinity, nitrate, temperature and dissolved oxygen. During the non-bloom period, variation of phytoplankton was small and could not be modeled successfully using the same four environmental factors used to create spring bloom models. Lagged phytoplankton measurements can be added as inputs to increase accuracy of all phytoplankton models.

Original languageEnglish
Title of host publicationProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Pages734-738
Number of pages5
DOIs
StatePublished - 2011
Event2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
Duration: Jul 26 2011Jul 28 2011

Publication series

NameProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Volume2

Conference

Conference2011 7th International Conference on Natural Computation, ICNC 2011
Country/TerritoryChina
CityShanghai
Period07/26/1107/28/11

Keywords

  • artificial neural network
  • community composition
  • dominant species
  • estuary
  • Skidaway River Estuary

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