• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.685-690
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• 2023

It was analyzed that the volume of deep excavation works adjacent to existing underground structures is increasing according to the population growth and density of cities. Currently, many underground structures and tracks are damaged by external factors, and the cause is analyzed based on the measurement results in the tunnel, and measurements are being made for post-processing, not for prevention. The purpose of this study is to analyze the effect on the deformation of the structure due to the excavation work adjacent to the urban railway track in use. In addition, the safety of structures is evaluated through machine learning techniques for displacement of structures before damage and destruction of underground structures and tracks due to external factors. As a result of the analysis, it was analyzed that the model suitable for predicting the structure management standard value time in the analyzed dataset was a polynomial regression machine. Since it may be limited to the data applied in this study, future research is needed to increase the diversity of structural conditions and the amount of data.

• Korean Journal of Environment and Ecology
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• v.26 no.4
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• pp.559-575
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• 2012

This study, targeting Namhan Mountain Fortress which was designated as a No. 57 national historic site and placed on the World Heritage Tentative List in 2010, was intended to identify the change of vegetation structures by reviewing past references, pictures, research data and additionally conducting a site survey. Also, it was designed to draw up measures for restoring vegetation suitable for historically and culturally valuable Namhan Mountain Fortress. According to the biotope mapping of study site, Quercus spp. forest distributed a greatest part of area with 40.8% of $2,611,823m^2$. Pinus densiflora forest, highly likely to go through ecological succession, was dispersed in the whole region of Cheongryangsan, the area from West Gate to North Gate and the ranges between South Gate to Cheongryangsan with taking 16.5%. Pinus densiflora forest with a low probability of succession amounted to 4.7% and was dispersed mainly in the forest behind Namhansan elementary school. Pinus densiflora going on the ecological succession is distributed a portion of 2.9%. And the currently dying out Pinus densiflora forest amounted to 2.1%. As a result of analysis of the vegetation structure for 19 years, the succession from Pinus densiflora forest to Pinus densiflora and succession from Quercus spp. mixed forest to Quercus spp. forest to Carpinus laxiflora forest were predicted. Additionally, Quercus spp. expanded its dominance over time. According to the characteristics of each classified zone, the site was categorized into $553,508m^2$ area of Pinus densiflora forest area for the landscape maintenance, $114,293m^2$ area of Pinus densiflora forest area for the landscape restoration, $205,306m^2$ area of Pinus densiflora forest area for the disclimax, and $1,169,973m^2$ area of Pinus densiflora forest area for inducing ecological succession.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.267-275
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• 2023

This study predicted rice harvest date in South Korea using 11-year (2012-2022) hindcasts based on dynamically downscaled 2m air temperature at subseasonal (1-month lead) timescale. To obtain high (5 km) resolution meteorological information over South Korea, global prediction obtained from the NOAA Climate Forecast System (CFSv2) is dynamically downscaled using the Weather Research and Forecasting (WRF) double-nested modeling system. To estimate rice harvest date, the growing degree days (GDD) is used, which accumulated the daily temperature from the seeding date (1 Jan.) to the reference temperature (1400℃ + 55 days) for harvest. In terms of the maximum (minimum) temperatures, the hindcasts tends to have a cold bias of about 1. 2℃ (0. 1℃) for the rice growth period (May to October) compared to the observation. The harvest date derived from hindcasts (DOY 289) well simulates one from observation (DOY 280), despite a margin of 9 days. The study shows the possibility of obtaining the detailed predictive information for rice harvest date over South Korea based on the dynamical downscaling method.