| Project/Area Number |
07680491
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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 |
Natural disaster science
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| Research Institution | Ehime University |
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Principal Investigator |
HATADA Yoshio Ehime Univ., Engineering, Research Assist., 工学部, 助手 (00164848)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAGUCHI Masataka Ehime Univ., Engineering, Professor, 工学部, 教授 (60027266)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1996: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1995: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | high waves and strong winds / duration of waves and winds / long term wave and wind data / peak values of wave height and wind speed / annual occurrence rate of duration / regression models / Monte-Carlo simulation / Weibull distribution / 長期資料分布 / 継続時間発生数 / 母数推定法 / 高波継続時間分布 / 極大波高分布 / 長期波高分布 / 対数線形回帰モデル / 確率波高推定法 / 長期波高スペクトル / 長期波高観測資料 |
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| Research Abstract |
Not only extremes of wave height and wind speed but also persistences of wave height and wind speed above a threshold value are important factors to be considered in design of coastal structures and planning of coastal defense works, but few attempts have been conducted because of lack of long term data with good quality. In this study, detail statistical analyzes have carried out for the long term data of wave height and wind speed gathered at many observation points around the coasts of Japan and the surrounding sea area. Regression models, in which independent variables are the dimensionless variance of the wave height or wind speed data and its dimensionless threshold value, are proposed for the estimation of probability distribution of long term wave height or wind speed and probability distributions of its persistence above a threshold and peak value during the duration period. Return period of high waves or strong winds with duration above a threshold, where these values are prescribed is evaluated with the combination of the above-mentioned three regression models. Next, an investigation has been made to find what kind of fitting method is preferable for the parameter estimation of a probability distribution to obtain a reliable estimate of statistics and how the sampling variability could be evaluated. The study uses 8 kinds of theoretical probability distribution including the Weibull and Gumbel distributions and 4 kinds of parameter estimation methods such as the probability weighted moment method and maximum likelihood method. Based on the statistical analysis with use of 3 or 4 methods for data generated by a Monte-Carlo simulation technique, the optimum method to be used for parameter estimation was determined for each distibution from the view points of minimizing bias and variance of statistics. Also applicability of the jackknife method and information matrix method to the estimation of variance of statistics were verified.
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