Neural network-based prediction of phytoplankton primary production

Habtom Ressom, Mohamad T. Musavi, Padma Natarajan

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Empirical models have been used to estimate primary production based on phytoplankton biomass and light intensity. In this paper, an alternative approach for estimating primary production using neural networks is proposed. The inputs to the neural network are chlorophyll, surface irradiance, sea surface temperature, and day length. The output of the network is the estimated primary production. The back-propagation learning algorithm is used to train the neural network. A single step learning with random presentation sequence is selected as the learning strategy. The data set used for this experiment is extracted from the Ocean Primary Productivity Working Group (OPPWG) database. The results show a significant decrease in the mean squared error of the log transformed primary production compared to the estimation obtained using a linear model and the vertically generalized production model (VGPM). The neural network-based models can deal with non-linear relationships more accurately, can effectively include variables that tend to covary non-linearly with the output variable, are flexible towards the choice of inputs, and are tolerant to noise. Hence, to improve the estimation of primary production, additional parameters can be easily incorporated in the neural network model, even though no a priori knowledge about the effect of these parameters is available. These important features of neural networks make them an ideal candidate for constructing primary production models for both case 1 and case 2 waters.

Original languageEnglish (US)
Pages (from-to)213-220
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4488
DOIs
StatePublished - Jan 1 2002
Externally publishedYes
EventOcean Optics: Remote Sensing and Underwater Imaging - San Diego, CA, United States
Duration: Aug 1 2001Aug 2 2001

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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