• Journal of Wetlands Research
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• v.17 no.2
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• pp.146-154
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• 2015

Domestic railway industry has grown in numbers, scale of railway ndustrial and operation because was focused on an environmentally sustainable transportation. However, it is not enough to treat and prevent heavy metals which occur as the railway operation increases. The heavy metals occurred when the operating railway and it will be flow into water system with rainfall effluent during rainfall. will flow out along with the rainfall effluent when rainfall comes. In case of a railway bridge, In particular, heavy metals were flow into the water system without any treatment from railway bridges where located nearby rivers and lakes. So, rainfall effluent from railway facilities was occurred pollution of water system. For the prevent of heavy metal runoff during rainfall, the adsorptivity of material in railway roadbed is important.In this study, adsorptivity of gravel which is main gravel and blast-furnace slag were conducted adsorption test and deducted Freundlich's and Langmuir's isothermal adsorption equations. Safety as railway subbase course material was evaluated using modeling. As a result, absorption amount of slag, Cd and Cu, was shown higher than gravel and Pb along with Zn showed higher absorption amount of gravel. However, absorption amount of slag was shown higher than gravel used as railway subbase course material as time passes by. Absorption features had more suitable determination coefficient of heavy metals in warm absorption type such as Langnmuir compared to warm absorption type like Freundlich. To add, they showed less transformation by about 10% compared to gravel in safety evaluation through modeling. This is a railway subbase course material that prevents water outflow of heavy metal thus we can know slag is needed to be used.

• The Journal of Engineering Geology
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• v.19 no.1
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• pp.33-41
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• 2009

In this study, some changes of soil characteristics in a field were analyzed to investigate the effect of heavy rainfall during rainy season. The heavy rainfalls were often induced geohazards like landslides. To do this, the reaching rainfall in the ground surface was investigated according to a condition of vegetation, and the change of soil characteristics induced by infiltrating rainfall was analyzed. The study site is a natural terrain located in Daedeok Science Complex. This site has same geology and soil condition whereas it has different vegetable condition. The rainfall records during the rainy season of 2006 and 2007 were selected. The rainfall records are based on the measuring date from Daejeon Regional Meteorological Administration adjacent to the study site. Also, the rainfall records according to the condition of vegetation were measured using rainfall measuring device made by ourselves. The soil tests were carried out about soil specimen sampled before and after rainfall, and then the change of soil characteristics related to rainfall and vegetation were analyzed. As the result, the density of vegetation was influenced by reaching rainfall quantity in the ground surface, and its influence intensity was decreased with rainfall intensity and rainfall duration. Also, it shows that degree of saturations, water contents, liquidities and shear resistances are directly influenced by heavy rainfalls.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.87-98
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• 2022

In recent years, frequent localized heavy rainfalls, which have a lot of rainfall in a short period of time, have been increasingly causing flooding damages. To prevent damage caused by localized heavy rainfalls, Hydrological Quantitative Precipitation Forecast (HQPF) was developed using the Local ENsemble prediction System (LENS) provided by the Korea Meteorological Administration (KMA) and Machine Learning and Probability Matching (PM) techniques using Digital forecast data. HQPF is produced as information on the impact of heavy rainfall to prepare for flooding damage caused by localized heavy rainfalls, but there is a tendency to overestimate the low rainfall intensity. In this study, we improved HQPF by expanding the period of machine learning data, analyzing ensemble techniques, and changing the process of Probability Matching (PM) techniques to improve predictive accuracy and over-predictive propensity of HQPF. In order to evaluate the predictive performance of the improved HQPF, we performed the predictive performance verification on heavy rainfall cases caused by the Changma front from August 27, 2021 to September 3, 2021. We found that the improved HQPF showed a significantly improved prediction accuracy for rainfall below 10 mm, as well as the over-prediction tendency, such as predicting the likelihood of occurrence and rainfall area similar to observation.

• Journal of Industrial Technology
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• v.21 no.A
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• pp.303-315
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• 2001

This study is a research result of investigating causes of landslides occurred at Uijongbu in Kyonggi Province, Korea. For works of this research, informations and data about landslides occurred at the site, geological and topographical informations were collected to analyze causes of landslides, and mapping landslides was performed by using results of field investigation. Data about rainfall during occurrence of landslides around Uijongbu was also used to find the effect of intense rainfall on occurrence of landslides. Based on informations obtained from field investigation and collected data, the scale and the pattern of landslides were analyzed and influencing factors on landslide such as intensity and duration of rainfall, topography, geologic condition, geotechnical engineering properties of ground, forestry were investigated statistically to find causes of landslides. On the other hands, for geotechnical engineering respects, slope stability analysis was performed for the typical sites chosen from the sites where the landslides occurred, using informations obtained from detailed topographical survey with total stations, field reconnaissance and results from laboratory tests.

• Atmosphere
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• v.27 no.4
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• pp.455-466
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• 2017

In Korea, property and human damages occur annually due to heavy precipitation during the summer. On August 8, 2015, heavy rainfall occurred in the Seoul metropolitan area due to an outflow boundary, and $77mmhr^{-1}$ rainfall was recorded in Gwangju, Gyeonggi Province. In this study, the simulation of the WRF numerical model is performed to understand the cause and characteristics of heavy rainfall using the Conditional Instability of the Second Kind (CISK), potential vorticity (PV), frontogenesis function, and convective available potential energy (CAPE) analyses, etc. Convective cells initiated over the Shandong Peninsula and located on the downwind side of an upper level trough. Large amounts of water vapor were supplied to the Shandong Peninsula along the southwestern edge of a high pressure system, and from the remnants of typhoon Soudelor. The mesoscale convective system (MCS) developed through CISK process and moved over to the Yellow Sea. The outflow boundary from the MCS progressed east and pushed cold pool eastward. The warm and humid air over the Korean Peninsula further enhanced convective development. As a result, a new MCS developed rapidly over land. Because of the latent heat release due to convection and precipitation, strong potential vorticity was generated in the lower atmosphere. The rapid development of MCS and the heavy rainfall occurred in an area where the CAPE value was greater than $1300Jkg^{-1}$ and the fronto-genesis function value of 1.5 or greater coincided. The analysis result shows that the MCS driven by an outflow boundary can be identified using CISK process.

• Atmosphere
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• v.22 no.4
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• pp.387-400
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• 2012

This study aims at examining the sensitivity of numerical simulations to the resolution of initial and boundary data, and to an application of WRF (Weather Research and Forecasting) 3DVAR (Three Dimension Variational data Assimilation). To do this, we ran the WRF model by using GDAS (Global Data Assimilation System) FNL (Final analyses) and the KLAPS (Korea Local Analysis and Prediction System) analyses as the WRF's initial and boundary data, and by using an initial field made by assimilating the radar data to the KLAPS analyses. For the sensitivity experiment, we selected a heavy rainfall case of 21 September 2010, where there was localized torrential rain, which was recorded as 259.5 mm precipitation in a day at Seoul. The result of the simulation using the FNL as initial and boundary data (FNL exp) showed that the localized heavy rainfall area was not accurately simulated and that the simulated amount of precipitation was about 4% of the observed accumulated precipitation. That of the simulation using KLAPS analyses as initial and boundary data (KLAPC exp) showed that the localized heavy rainfall area was simulated on the northern area of Seoul-Gyeonggi area, which renders rather difference in location, and that the simulated amount was underestimated as about 6.4% of the precipitation. Finally, that of the simulation using an initial field made by assimilating the radar data to the KLAPS using 3DVAR system (KLAP3D exp) showed that the localized heavy rainfall area was located properly on Seoul-Gyeonggi area, but still the amount itself was underestimated as about 29% of the precipitation. Even though KLAP3D exp still showed an underestimation in the precipitation, it showed the best result among them. Even if it is difficult to generalize the effect of data assimilation by one case, this study showed that the radar data assimilation can somewhat improve the accuracy of the simulated precipitation.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Journal of the Korean GEO-environmental Society
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• v.5 no.3
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• pp.41-50
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• 2004

The study of seepage behavior is very important to slope stability of road landfill for heavy rainfall season. This study is done to propose more stable of road landfill based on analysis of seepage behavior and slope stability for some cases of road landfill. The selected sections of collapsed road landfill are most general case of road landfill, a case is landfill on the ground area and another case is on the slope area. The results of this study is summarized as follows. It is founded that the road landfill on the ground area is increased saturation region due to rainfall infiltration, and the seepage behavior of road landfill is solved by theory of unsaturated flow. The road landfill is more unstable due to rainfall infiltration at the slope surface, especially during heavy rainfall. The case of road landfill on the slope area is analyzed in consideration of slope surface infiltration, and it is founded that the slope instability is increased because of rainfall infiltration. The drain layer located on the original ground which made by more permeable materials could be good action of slope stability in the case of road landfill on the slope area.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.4
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• pp.11-20
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• 2007

Most natural disasters in Korea are caused by meteorological natural phenomena, which include storms, heavy rains, heavy snow, hail, tidal waves, and earthquakes. Rainfall is the most frequent cause of disasters and accounts for about 80% of all disasters. Particularly in recent years, Korea has seen annual occurrences of natural disasters associated with landslides (slope and retaining wall collapse and burying) due to meteorological causes from the increasing intensity of heavy rains including local heavy rainfalls. In Korea, it is critical to analyze the characteristics of landslides according to rainfall characteristics and to take necessary and proper measures for them. This study assessed the possibility of landslides in the Gangwon region with a geographic information system by taking into account the inducer factors of landslides and the maximum continuous rainfall of each area. It also analyzed areas susceptible to landslides and checked the distribution of landslide-prone areas by considering the rainfall characteristics of those areas.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.125-135
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• 2019

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.