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
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
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