| Project/Area Number |
06650437
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
System engineering
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| Research Institution | Akita University |
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Principal Investigator |
NISHIDA Makoto Akita University・Mining College Professor, 鉱山学部, 教授 (70091816)
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| Project Period (FY) |
1994 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1995: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1994: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Remote sensing data / Water quality / Estimatin map / Fuzzy statistics / Fuzzy level-slice method / Knowlegement / Water flow / Finite element method / リモートセンシング / 水質分布 / ファジィ回帰分析 / ファジィ統計 / レベルスライス処理 / ファジィレベルスライス処理 / 知識ベース / 流動計算 / 衛星画像 / 可能性分布図 |
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| Research Abstract |
This report presents the results of using the fuzzy regression model to analyze the water quality from the satellite data. A case study has been conducted and the results of the fuzzy regression model have been compared with the correlation coefficient of the conventional statistical model. To draw the possibility distribution map of the water quality, a new method with the fuzzy level-slice is developed in this research.
It is shown by statistical analysis that the CCT count number of the remote sensing data has no relation to the water quality. However, the fuzzy regressin analysis shows that the CCT count number is related to several parameters about the water quality. It has also been observed that an investigation of the extreme variation among the objective function of fuzzy regressin analysis has an effect on the selection of the irregular point of the water quality data. Therefore, it has been clarified that the fuzzy regression model is applicable to the analysis of the water quality. In additon, it is pssible to introduce the knowlegement of an expert in the investigation about water quality into the fuzzy statistical process.
The estimation map of the water quality has been drown by using the fuzzy level-slice method. It is shown that the estimation map corresponds with the forecasted range from the actual data of water quality. It is also becomes clear that the distribution of the pollution level on the estimation map corresponds with the knowlege of the expert of the water quality. In additon, fuzzy level-slice method is possible to classify the water quality by ten level. Therefore, it can be concluded that the fuzzy level-slice method is useful technique for drawing the estimation map of the water quality. An analysis algorithm for the water flow due to a wind has been eeveloped by the finite element method. It has clarified that the time series forecasting results about the water flow corresponded with the estimation map of the water quality.
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