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Yokota Motohiro


Prediction of Standing Stock Variation for Phytoplankton using Neural Networks

Journal of National Fisheries University Volume 56 Issue 3 Page 251-259
published_at 2008-02
56-3-251-259.pdf
[fulltext] 894 KB
Title
ニューラルネットワークによる植物ブランクトン現存量変動の予測
Prediction of Standing Stock Variation for Phytoplankton using Neural Networks
Creators Yokota Motohiro
Creators Taira Yuichiro
Creators Morimoto Eiji
Source Identifiers [PISSN] 0370-9361
Creator Keywords
Neural network Prediction Biomass Phytoplankton Chlorophylls Diatoms
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.
Languages jpn
Resource Type departmental bulletin paper
Publishers National Fisheries University
Date Issued 2008-02
File Version Version of Record
Access Rights open access
Relations
[ISSN]0370-9361