• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1365-1371
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model. The slope hazards data of Seoul and Kyonggi Province were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. The statistical analyses using the decision tree model were applied to the entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320m, respectively.

• The Journal of Engineering Geology
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• v.18 no.1
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• pp.45-54
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model, which is one of the statistical analysis methods. The slope hazards data of Seoul and Kyonggi Province, which were induced by heavy rainfall in 1998, were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. Among these data, the number of data occurred slope hazards was 34 sections and the number of data non-occurred slope hazards was 27 sections. The statistical analyses using the decision tree model were applied to chi-square statistics, gini index and entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320 m, respectively.

• International Area Studies Review
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• v.13 no.2
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• pp.226-253
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• 2009

The study analyzes the trend of regional economic development disparity in China, sets up research period from 1952 to 2008, and uses the after-modified regional GDP data by the first national economic census in 2004. The results as follow. Firstly, the Coefficient of variation(CV) with after-modified GDP data lower than the pre-modified data. Secondly, generally speaking, after-reform and open period's disparity lower than pre-reform and open period. In particular, the regional development disparity increased slowly after 1990, not rapidly. Third, the new cycle of the inverse-U type is appeared from 2002. Fourth, compared with Herfindhal-Hirschman index(HHI) and Theil Entrophy index(TEI), the lower level regions more affect to reduce the disparity in 1980s, and it also affect to reduce the disparity after 2000. Fifth, the convergence hypothesis test finds that the regional economic development disparity has been converged in 1978-2008. Sixth, the inverse-U type hypothesis not has statistical significance, from 1952 to 2008, but it has statistical significance from 1991 to 2008. This result same as the CV and the convergence test.