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

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.

[ISSN]0370-9361