Application of an artificial neural network formulated to predict the outbreak of musty odor and control it
| Project/Area Number |
12650543
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Civil and environmental engineering
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
HOSOMI Masaaki Tokyo Univ. of Agr. & The., Professor, 工学部, 教授 (90132860)
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| Project Period (FY) |
2000 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2001: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2000: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | neural / network / model / musty odor / WATARASE RESERVDIR prediction / 2-mettly lisoborneol / 渡瀬遊水池 |
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| Research Abstract |
Outbreak of musty odor in reservoirs for drinking water caused serious problems with regard to water-supply such as musty taste of tap water. Control of musty odor is one of the most important tasks in water quality management of reservoirs. This paper describes the novel application of an artificial neural network (ANN) model based on the back-propagation method formulated to predict outbreak of a musty-odorous compound, 2-methylisoborneol (2-MIB), in Watarase Reservoir, which is one of drinking water resources for Tokyo area in Japan. By using ANN model, we constructed the model predicting absolute values of the 2-MIB concentrations in Watarase freshwater reservoir. As the input layer data, ANN model used various data obtained from 1992 to 1997, i.e. meteorological conditions, water qualities, nutrients, phytoplanktons, and operational conditions of Watarase Reservoir. Comparing the absolute values of 2-MIB concentrations calculated by the ANN model with those observed, it should be noted that the timing of outbreak of 2-MIB was well-predicted by the ANN model. Prediction of the absolute values of 3 days after- 2-MIB concentrations resulted in 0.65 of the correlation coefficient, thereby indicating good feasibility of predicting 2-MIB concentrations by the ANN model.
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Report
(3 results)
Research Products
(3 results)
