• Title, Summary, Keyword: 최우법

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Comparison of Methods for the Analysis Percentile of Seismic Hazards (지진재해도의 백분위수 분석 방법 비교)

  • Rhee, Hyun-Me;Seo, Jung-Moon;Kim, Min-Kyu;Choi, In-Kil
    • Journal of the Earthquake Engineering Society of Korea
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    • v.15 no.2
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    • pp.43-51
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    • 2011
  • Probabilistic seismic hazard analysis (PSHA), which can effectively apply inevitable uncertainties in seismic data, considers a number of seismotectonic models and attenuation equations. The calculated hazard by PSHA is generally a value dependent on peak ground acceleration (PGA) and expresses the value as an annual exceedance probability. To represent the uncertainty range of a hazard which has occurred using various seismic data, a hazard curve figure shows both a mean curve and percentile curves (15, 50, and 85). The percentile performs an important role in that it indicates the uncertainty range of the calculated hazard, could be calculated using various methods by the relation of the weight and hazard. This study using the weight accumulation method, the weighted hazard method, the maximum likelihood method, and the moment method, has calculated the percentile of the computed hazard by PSHA on the Shinuljin 1, 2 site. The calculated percentile using the weight accumulation method, the weighted hazard method, and the maximum likelihood method, have similar trends and represent the range of all computed hazards by PSHA. The calculated percentile using the moment method effectively showed the range of hazards at the source which includes a site. This study suggests the moment method as effective percentile calculation method considering the almost same mean hazard for the seismotectonic model and a source which includes a site.

A Study on Statistical Modeling of Spatial Land-use Change Prediction (토지이용 공간변화 예측의 통계학적 모형에 관한 연구)

  • 김의홍
    • Spatial Information Research
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    • v.5 no.2
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    • pp.177-183
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    • 1997
  • S1he concept of a class in the land-use classification system can be equally applied to a class in the land-use-change classification. The maximum likelihood method using linear discriminant function and Markov transition matrix method were integrated to a synthetic modeling effort in order to project spatial allocation of land-use-change and quantitative assignment of that prediction as a whole. The algorithm of both the multivariate discriminant function and the Markov chain matrix were discussed and the test of synthetic model on the study area was resulted in the projection of '90 year as well as '95 year land -use classification. The accuracy and the issue of modeling improvement were discussed eventually.

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