Construction of a Model for Water Purification Mechanisms in a River by Using a Neural Network Approach

Tomoo AOYAMAa, Junko KAMBEb,c, Aiko YAMAUCHId and Umpei NAGASHIMAc,e*

aFaculty of Technology, Miyazaki University
Gakuen Kihanadai Nishi, Miyazaki, 889-2192, Japan,
bFaculty of Foreign Language, Daito Bunka University
1-9-1 Takashimadaita, Itabashi, Tokyo 175-8571, Japan
cCore Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST)
Kawaguchi Center Building,4-1-8, Honcho, Kawaguchi, Saitama 332-0012 Japan
dGraduate School of Pharmaceutical Sciences, University of Tokushima
1-78 Sho-machi, Tokushima 770-8505, Japan
eResearch Institute of Computational Science, National Institute of Advanced Industrial Science and Technology
1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan

(Received: November 9, 2006; Accepted for publication: February 27, 2007; Published on Web: May 15, 2007)

A model for purification mechanisms in a river was proposed to express changes in the values of BOD (Biochemical Oxygen Demand), COD (Chemical Oxygen Demand), T-N (Total Nitrogen), and T-P (Total Phosphorous) as the combination of inflows, streams, and weirs. Because expressions for these functions are unknown, the model first constructs multi-layer neural-network functions based on observations, and then uses the derivatives to evaluate the cause and effect of pollution.
For data of the Tamagawa through Tokyo, Japan in 2002, the model suggested that the cause of pollution is inflows from sewage, the stream of the Tamagawa has purification functions for COD and T-P, but has little ability for T-N, and the weirs have purification for COD, but have no purification for T-N and T-P. The results from the model were consistent with common sense for the water quality, and thus, there was no failure in the model.

Keywords: River Model, Water Purification, Neural Network, Derivatives


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