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A depth-resolved artificial neural network model of marine phytoplankton primary production

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  • Mattei, F.
  • Franceschini, S.
  • Scardi, M.

Abstract

Marine phytoplankton primary production is an extremely important process and its estimates play a major role not only in biological oceanography, but also in a broader context, due to its relationship with oceanic food webs, energy fluxes, carbon cycle and Earth’s climate.

Suggested Citation

  • Mattei, F. & Franceschini, S. & Scardi, M., 2018. "A depth-resolved artificial neural network model of marine phytoplankton primary production," Ecological Modelling, Elsevier, vol. 382(C), pages 51-62.
    • Handle: RePEc:eee:ecomod:v:382:y:2018:i:c:p:51-62
    DOI: 10.1016/j.ecolmodel.2018.05.003
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    References listed on IDEAS

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    1. Iebeling Kaastra & Milton S. Boyd, 1995. "Forecasting futures trading volume using neural networks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(8), pages 953-970, December.
    2. Holmlund, Cecilia M. & Hammer, Monica, 1999. "Ecosystem services generated by fish populations," Ecological Economics, Elsevier, vol. 29(2), pages 253-268, May.
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    Cited by:

    1. Mattei, F. & Buonocore, E. & Franzese, P.P. & Scardi, M., 2021. "Global assessment of marine phytoplankton primary production: Integrating machine learning and environmental accounting models," Ecological Modelling, Elsevier, vol. 451(C).
    2. Sunayana & Komal Kalawapudi & Ojaswikrishna Dube & Renuka Sharma, 2020. "Use of neural networks and spatial interpolation to predict groundwater quality," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 2801-2816, April.
    3. Mattei, F. & Scardi, M., 2020. "Embedding ecological knowledge into artificial neural network training: A marine phytoplankton primary production model case study," Ecological Modelling, Elsevier, vol. 421(C).

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