• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.1
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• pp.11-20
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• 2008

In probabilistic seismic analysis of nonlinear structural system, dynamic analysis is performed to obtain the distribution of the response estimate using input ground motion time histories which correspond to a given seismic hazard level. This study investigates the differences in the distribution of the responses and the failure probability according to input ground motion models. Two types of input ground motion models are considered: real earthquake records scaled to specified intensity level and artificial input ground motion fitted to design response spectrum. Simulation results fir a nonlinear SDOF system demonstrate that the spectrum matched input ground motion produces larger failure probability than those of scaled input ground motion due to biased responses. Such tendency is more remarkable in the site of soft soil conditions. Analysis results show that such difference of failure probability is due to the conservative estimation of design response spectrum in the range of long period of ground motion.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.260-260
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• 2016

Soil erosion is a very serious problem from agricultural as well as environmental point of view. Various computer models have been used to estimate soil erosion and assess erosion control practice. Universal Soil loss equation (USLE) is a popular model which has been used in many countries around the world. Erosivity (USLE R-factor) is one of the USLE input parameters to reflect impacts of rainfall in computing soil loss. Value of R factor depends upon Energy (E) and maximum rainfall intensity of specific period ($I30_{max}$) of that rainfall event and thus can be calculated using higher temporal resolution rainfall data such as 10 minute interval. But 10 minute interval rainfall data may not be available in every part of the world. In that case we can use hourly rainfall data to compute this R factor. Maximum 60 minute rainfall ($I60_{max}$) can be used instead of maximum 30 minute rainfall ($I30_{max}$) as suggested by USLE manual. But the value of Average annual R factor computed using hourly rainfall data needs some correction factor so that it can be used in USLE model. The objective of our study are to derive relation between averages annual R factor values using 10 minute interval and hourly rainfall data and to determine correction coefficient for R factor using hourly Rainfall data.75 weather stations of Korea were selected for our study. Ten minute interval rainfall data for these stations were obtained from Korea Meteorological Administration (KMA) and these data were changed to hourly rainfall data. R factor and $I60_{max}$ obtained from hourly rainfall data were compared with R factor and $I30_{max}$ obtained from 10 minute interval data. Linear relation between Average annual R factor obtained from 10 minute interval rainfall and from hourly data was derived with $R^2=0.69$. Correction coefficient was developed for the R factor calculated using hourly rainfall data.. Similarly, the relation was obtained between event wise $I30_{max}$ and $I60_{max}$ with higher $R^2$ value of 0.91. Thus $I30_{max}$ can be estimated from I60max with higher accuracy and thus the hourly rainfall data can be used to determine R factor more precisely by multiplying Energy of each rainfall event with this corrected $I60_{max}$.

• Journal of Korea Water Resources Association
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• v.52 no.6
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• pp.381-395
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• 2019

The purpose of this study is to predict agricultural reservoir storage rate (RSR) in a month. This algorithm was developed by multiple linear regression model (MLRM) which included the past 3 months RSRs data and the future climate change scenarios. In order to improve use of predicted RSR, this study need the severe criteria in terms of drought. So, the predicted RSR was indexed as the 3 months reservoir drought index (RDI3) and then it was disaggregated into drought duration, severity, and intensity. For the future RSR estimation by climate change scenarios, the 6 RCP 8.5 scenarios of HadGEM2-ES, CESM1-BGC, MPI-ESM-MR, INM-CM4, FGOALS-s2, and HadGEM3-RA were used in three future evaluation periods (S1: 2011~2040, S2: 2041~2070, S3: 2071~2099). The future S3 period of HadGEM2-ES scenario which has the biggest increase in precipitation and temperature showed the largest decrease to 60.2% among the 6 scenarios compared to the historical RSR (1976~2005) 77.3%. In contrast, INM-CM4 scenario which has smallest changes in precipitation and temperature in S3 period showed the smallest decrease to 72.8%. For the CESM1-BGC and MPI-ESM-MR, FGOALS-s2, and HadGEM3-RA, the S3 period RSR showed 72.6%, 72.6%, 67.4%, and 64.5% decrease respectively. The future severe drought condition of RDI3 below -0.25 showed the increase trend for the number and severity up to -2.0 during S3 period.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.349-358
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• 2013

In this study, the newest technology available to reduce GHG emissions, which can be applicable in energy industries of the future that has large reduction obligations by energy target management and large intensity of GHG emissions, has been investigated by searching the technical characteristics of each technology. The newest technology to reduce GHG emissions in the field of power generation and energy can be mainly classified into the improvement of efficiency, CCS, and gas combined-cycle technology. In order to improve the reliability of the GHG emission factor obtained from the investigation process, it has been compared to the technology-specific GHG emission factor derived from the estimated amount of emissions. Then the GHG abatement measures, using the derived estimation of factor, by using the newest technology to reduce GHG emissions have been predicted. As a result, the GHG reduction rate by technology of CCS development has been expected to be the largest more than 30%, and the abatement rate by technology of coal gasified fuel cell and pressurized fluidized-bed thermal power generation has been showed more than 20%. If the effective introduction of the newest technology and the study of its characteristics is continued, and properly applied for future GHG emissions, it can be prospected that the national GHG reduction targets can be achieved in cost-efficient way.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1127-1134
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• 2018

According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), climate change increases the frequency of abnormal weather phenomenon. As the frequency of abnormal weather phenomenon increases, frequency of disasters related to water resources such as floods and droughts also increases. Drought is the main factor that directly affects water supply. Recently, the intensity of drought and the frequency of drought occurrence have increased in Korea. So, there is a need for water resource securing technology for stable water supply. Korean Water Plan mentioned that water reserves concept is necessary for stable water supply. Most multi-purpose reservoirs in Korea have emergency storage in addition to conservation storage used for water supply. However, there is no clear use standard for emergency storage. This study investigated the use of reservoir reserves for stable water supply. In order to consider the climate change impact, the AR5-based hydrological scenario was used as inflow data for the reservoir simulation model. Reservoir simulations were carried out in accordance with the utilization conditions of emergency storage and water supply adjustment standard. The optimal reserves for each multi-purpose reservoirs was estimated using simulation results.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.25-36
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• 2020

An increasing frequency and intensity of natural disasters have been observed due to climate change. To better prepare for these, the MOIS (ministry of the interior and safety) announced a comprehensive plan for minimizing damages associated with natural disasters, including drought and heavy snowfall. The spatial-temporal pattern of snowfall is greatly influenced by temperature and geographical features. Heavy snowfalls are often observed in Gangwon-do, surrounded by mountains, whereas less snowfall is dominant in the southern part of the country due to relatively high temperatures. Thus, snow depth data often contains zeros that can lead to difficulties in the selection of probability distribution and estimation of the parameters. A generalized mixture distribution approach to a maximum snow depth series over the southern part of Korea (i.e., Changwon, Tongyeoung, Jinju weather stations) are located is proposed to better estimate a threshold (𝛿) classifying discrete and continuous distribution parts. The model parameters, including the threshold in the mixture model, are effectively estimated within a Bayesian modeling framework, and the uncertainty associated with the parameters is also provided. Comparing to the Daegwallyeong weather station, It was found that the proposed model is more effective for the regions in which less snow depth is observed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.373-385
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• 2022

The drum cutter, which is used in the form of an attachment of a excavator, is very useful in that it can be used in connection with a excavator that can perform various tasks in the field. This study estimated the load and torque acting on the drum cutter attachment by measuring the hydraulic pressure and strain that appear during excavation on the exposed rock slope using the drum cutter installed in the excavator. Working conditions such as the operation angle between the boom and arm of the excavator were divided into eight working modes. And as a result of analyzing the variations in hydraulic pressure and action force according to the working mode, it was confirmed that the hydraulic pressure and flow rate can be driven without any problems within the range considered in the manufacturing specifications of the drum cutter. The average load and torque acting on the drum cutter were within the range of the manufacturing specifications, but the maximum load was up to four times the specification. Because sumping was not properly performed due to the high ground strength and the ground included discontinuous surfaces in some locations, no trend of load and torque was found depending on the angle between the boom and arm of the excavator. However, it is believed that this result can be used to determine the range of loads and torques that appear on the drum cutter when excavating a high-intensity rock.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.217-229
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• 2023

Forest projects often apply construction industry labor rates, without considering the demanding work conditions and labor intensity unique to forest operations, resulting in workplace issues. This study aims to analyze forest operations' characteristics and wage survey methods in other fields to establish a framework for surveying the wages of forest workers. The developed framework was tested through direct surveys conducted with all forest operation companies. Survey items included actual wages by occupation, identification and removal of outliers using quartile deviation, and occupation-based wage calculation. Results revealed that the appropriate wages for 2022 were as follows: KRW 163,376 for general workers, KRW 221,407 for special workers, KRW 250,045 for work leaders, and KRW 239,863 for wood cutters. These figures were 16.27% higher than those derived from the standard construction wage survey. The developed framework was validated by comparing the appropriate wages with both the standard construction wage survey and the forestry workers' wage. The results indicated that the wages calculated using the developed framework were 4.5% more similar to the forestry workers' wage compared with those from the standard construction wage survey. Consequently, the standard construction wage survey was deemed unsuitable for forest projects. To ensure efficient forest operations, it is imperative to conduct wage surveys using the developed framework over multiple years to accumulate sufficient data.