Prediction of standing stock variation for phytoplankton using neural networks
2008
Yokota, M.(National Fisheries Univ., Shimonoseki, Yamaguchi (Japan)) | Taira, Y. | Morimoto, E.
This study develops a prediction method for a phytoplankton standing stock (chlorophyll-a and diatomaceous cell number) in a fish farm using data obtained from experiments for bottom sediment improvement (environmental monitoring research) in the Katada Culture Farm in Ago Bay, Mie prefecture. Results show that the fluctuation of a phytoplankton standing stock from the surface layer (a depth of 0.5 m) to the bottom layer (a depth of 0.5 m on the bottom face) can be estimated using a neural network whose inputs are water depth, water temperature, salinity, dissolved oxygen, pH, chemical oxygen demand, hours of sunshine, and respective amounts of precipitation and mean air temperature.
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