PREDICTION MODEL OF TASTE-AND-ODOR EVENTS IN KAMAFUSA RESERVOIR

• HAI THUAN Duong

  Graduate School of Engineering, Tohoku University

• UMEDA Makoto

  Graduate School of Engineering, Tohoku University

• MATSUKAWA Masahiko

  Kamafusa Dam Office, Ministry of Land, Infrastructure and Transportation

• TANAKA Hitoshi

  Graduate School of Engineering, Tohoku University

抄録

Artificial neural network (ANN) is applied to a predictive model for the intermittent occurrence of taste-and-odor problem in the source of drinking water in Kamafusa Reservoir, Japan. To predict the temporal variation of 2-methylisoborneol (MIB) concentration, which triggers taste-and-odor problem, ten-year data of water quality from continuous water quality monitor as well as using the microscopic analysis data of planktonic cyanobacteria. The model examined in this paper is capable of reproducing the trend of evolution of MIB concentration and hence the intermittent occurrence of taste-and-odor events observed in Kamafusa Reservoir. Thus the model can be used as a decision-making tool for reservoir management office in measuring and treating the quality of water.

収録刊行物

• 土木学会論文集Ｂ１（水工学）

  土木学会論文集Ｂ１（水工学） 68 (4), I_289-I_294, 2012

  公益社団法人 土木学会

キーワード

• Taste-and-odor problem
• MIB
• Kamafusa Reservoir
• artificial neural network

詳細情報

• CRID: 1390282680328407552
• NII論文ID: 130004557965
• DOI: 10.2208/jscejhe.68.i_289
• ISSN: 2185467X
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可