Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Wei Zheng; Honghua Shi; Xikun Song; Dongren Huang; Long Hu

doi:10.1007/s00343-012-1216-6

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Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Updated：2023-05-16

- Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables
- Journal of Oceanology and Limnology Vol. 30, Issue 5, Pages: 843-851(2012)
- Affiliations：
  
  1. First Institute of Oceanography State Oceanic Administration
  2. Institute of Oceanography Chinese Academy of Sciences
  3. Monitoring Center of Marine Environment and Fishery Resources of Fujian Province
  4. School of Mathematical Sciences Fudan University
- Author bio：
- Funds：
- DOI：10.1007/s00343-012-1216-6
  CLC：
- Online First：05 September 2012，
  
  Published：2012
- Accepted：
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Zheng, W., Shi, H., Song, X. et al. Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables., Chin. J. Ocean. Limnol. 30, 843–851 (2012). https://doi.org/10.1007/s00343-012-1216-6
DOI：

Zheng, W., Shi, H., Song, X. et al. Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables., Chin. J. Ocean. Limnol. 30, 843–851 (2012). https://doi.org/10.1007/s00343-012-1216-6 DOI：

摘要

Abstract

Prediction and sensitivity models

to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay

Fujian

China

were developed using a back propagation (BP) network. The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train

test and build a three-layer BP artificial neural network with multi-input and single-output. Ten water quality parameters were used to forecast phytoplankton biomass (measured as chlorophyll-a concentration). Correlation coefficient between biomass values predicted by the model and those observed was 0.964

whilst the average relative error of the network was −3.46% and average absolute error was 10.53%. The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass. A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass. Indicators were classified according to the sensitivity of response and its risk degree. The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH

sea surface temperature

sea surface salinity

chemical oxygen demand and ammonium.

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Keywords

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