Modeling Of Denitrification Rates In Eutrophic
Wetlands By Artificial Neural Networks
Author(s)
K. Song, M.-Y. Song, T.-S. Chon & H. Kang
Abstract
Eutrophication can be controlled by denitrification which is complex microbial
processes converting nitrate to nitrogen gas in water body. Various
environmental factors such as oxygen, available carbon and pH are known to
regulate denitrification rate. However, those controlling variables affect
denitrification rates nonlinearly. Further, interactions between those factors
hinder a good prediction on the rate. As such, conventional mechanistic
modeling of denitrification often fails to fit with data collected from fields.
In this study, we applied artificial neural networks to elucidate complex
relationships between denitrification rate and environmental factors. A Multi-Layer
Perceptron network based on the back-propagation algorithm was utilized
for prediction of denitrification rate. High predictability of denitrification was
achieved with R=0.910 with the trained network. Subsequently, sensitivity
analysis was carried out to evaluate factors predominantly controlling
denitrification rates. A sensitivity analysis exhibited that DO exert a dominant
controlling effect on denitrification rate over other environmental factors.
Keywords: denitrification, eutrophic wetland, artificial neural networks.
1 Introduction
Aquatic ecosystems near agricultural or urban systems can be polluted by a high
loading of nitrogen, which often results in eutrophication in summer season.
Since eutrophication causes oxygen depletion, fish kill and odor, many studies
Keywords
denitrification, eutrophic wetland, artificial neural networks.
Related Book
Other papers in this volume
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