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Prediction of Chemical Oxygen Demand Variation in an Enclosed Sea Area using Neural Networks

Journal of National Fisheries University Volume 57 Issue 1 Page 21-27
published_at 2008-10
57-1-21-27.pdf
[fulltext] 1010 KB
Title
ニューラルネットワークを用いた閉鎖性海域における化学的酸素要求量の変化予測
Prediction of Chemical Oxygen Demand Variation in an Enclosed Sea Area using Neural Networks
Creators Yokota Motohiro
Creators Taira Yuichiro
Creators Morimoto Eiji
Creators Ezoe Satoru
Creators Ogawa Kazuo
Source Identifiers [PISSN] 0370-9361
Creator Keywords
Neural network Prediction Water quality Chemical oxygen demand Enclosed sea area Sensitivity analysis
This study develops a prediction method for a chemical oxygen demand 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 chemical oxygen demand from the surface layer (a depth of 0.5m) to the bottom layer (a depth of 0.5m on the bottom face) can be estimated using a neural network whose inputs are water depth, water temperature, salinity, dissolved oxygen, pH, chlorophyll-a, hours of sunshine, and respective amounts of precipitation and mean air temperature. When the sensitivity analysis was carried out to clarify the contribution of each environmental factor for the chemical oxygen demand, it was affected considerably by weather conditions (hours of sunshine, precipitation, water temperature, etc.), salinity, and chlorophyll-a.
Languages jpn
Resource Type departmental bulletin paper
Publishers National Fisheries University
Date Issued 2008-10
File Version Version of Record
Access Rights open access
Relations
[ISSN]0370-9361