• Journal of the Korean Geotechnical Society
• /
• v.28 no.4
• /
• pp.23-33
• /
• 2012

Recently, landslide disasters caused by severe rain storms and typhoons have been frequently reported. Due to the geomorphologic characteristics of Korea, considerable portion of urban area and infrastructures such as road and railway have been constructed near mountains. These infrastructures may encounter the risk of landslide and debris flow. It is important to evaluate the highly risky locations of landslide and to prepare measures for the protection of landslide in the process of construction planning. In this study, a landslide-risk prediction equation is proposed based on the statistical analysis of 423 landslide data set obtained from field surveys, disaster reports on national road, and digital maps of landslide area. Each dataset includes geomorphologic characteristics, soil properties, rainfall information, forest properties and hazard history. The comparison between the result of proposed equation and actual occurrence of landslide shows 92 percent in the accuracy of classification. Since the input for the equation can be provided within short period and low cost, and the results of equation can be easily incorporated with hazard map, the proposed equation can be effectively utilized in the analysis of landslide-risk for large mountainous area.

• The Journal of Engineering Geology
• /
• v.8 no.2
• /
• pp.115-130
• /
• 1998

An analysis on landslide types and susceptibilities associated with geomorphic characteristics has been conducted with 916 landslide inventories in Yeonchon-Chulwon District, where two day's heavy rainfall was concentrated on July, 1996. The precipitation during the 2 days, which is equivalent to 0.372 of event cofficient, can cause large landslides based on Olivier's equation. Sliding materials are dominantly composed of debris mixed with rock fragments and soil derived from colluvium and residual soils. 66% of the landslides are belong to debris flow md 23% are due to sediments flow, in accordance with the classification of sliding materials. Most of landslides(> 90%) are small and shallow, less than l00m in length and about 1m in depth, and classified as transitional type. Granite is more susceptible as much as 4.7 times than metamorphic rocks and 2.7 times than volcanic rocks, probably due to higher weathering grade of granite. The highest landslide frequency is concentrated on the areas between 200 and 300m in height and on the slopes between $10-20^{\circ}$ in dgree. More than 50% of landslides occurred under these geomorphic conditions. Consequently, colluviums and residual soils distributed on the gentle slopes are most susceptible to the landslides of the area.

• Journal of Soil and Groundwater Environment
• /
• v.12 no.5
• /
• pp.86-97
• /
• 2007

The principal component analysis was performed to identify the general characteristics of groundwater level changes from 202 deep and 112 shallow wells monitoring data, respectively, which came from the National Groundwater Monitoring Stations operated by KWATER with time spans of 156 continuous weeks from 2003 to 2005. Eight principal components, which accounted for 80% of the variability of the original time series, were extracted for water levels of shallow and deep monitoring wells. As a result of cluster analysis using the loading value of three principal components for shallow wells, shallow monitoring wells were divided into 3 groups which were characterized with a response time to rainfall (Group 1: 4.6 days, Group 2: 24.1 days, Group 3: 1.4 days), average long-term trend of water level (Group 1: $2.05{\times}10^{-4}$ m/day, Group 2: $-7.85{\times}10^{-4}$ m/day, Group 3: $-3.51{\times}10^{-5}$ m/day) and water level difference (Group 1 < Group 2 < Group 3). Additionally, they showed significant differences according to a distance to the nearest stream from well (Group 3 < Group 2 < Group 1), topographic slope of well site (Group 3: plain region, Group 1: mountainous region) and groundwater recharge rate (Group 3 < Group 2 < Group 1) with a p-value of 0.05.

• Journal of Korea Water Resources Association
• /
• v.50 no.10
• /
• pp.653-660
• /
• 2017

The SCS-CN (Soil Conservation Service-Curve Number) method has been practically applied for estimating the effective precipitation. The CN is used to be determined according to the land use condition based on the US standard. However, there are two distinctive differences between U.S. and Korean land use conditions: mountainous (forest) and rice paddy area that cover more than 70% of the Korean territory. The previous work proposed to use 79 for rice paddy area, regardless of the soil type. Because US SCS's goal was originally to increase crops, the SCS classification standard provides only for woods and there are no criteria to distinguish the wood and forest. To determine the CN for forest, alternatively the U.S. Forest Service criteria have been employed in practice considering hydrologic condition class. In this study, we investigated the change of the forest CN using the observed rainfall - runoff data within the target area. The results indicated that the CN for forest was suitable for HC=1, and the corresponding CNs were redetermined between 54 and 55.

• Journal of Korea Water Resources Association
• /
• v.49 no.5
• /
• pp.373-380
• /
• 2016

The NRCS-CN (Natural Resources Conservation Service-Curve Number) method has been practically applied for estimating the effective precipitation. However, there are no criteria which reflect the geographic characteristics of Korea having more than 70% of mountainous and rice paddy areas, leading to significant errors in runoff calculation. Thus, it is required to estimate the runoff curve number considered Korea land use classification, however there are practical difficulties to conduct the accurate research and experimentation. In this study, after selecting target areas (urban, agriculture, forest), we performed the runoff analysis to redetermine CN values for the selected basins. To do this, curve numbers for soil type A were estimated using genetic algorithm, and then curve numbers for soil type (B, C, D) were estimated using CN aligner equation. Comparing the initial curve numbers with the estimated curve numbers, it was observed that the slightly differences at Chunwang(0), Choonyang(-1), Janggi(-3). Through the above process, this study proposed new curve numbers to reflect observed rainfall-runoff.

• The Journal of Engineering Geology
• /
• v.27 no.2
• /
• pp.153-164
• /
• 2017

The geological factors for causing ground subsidence are very diverse. It can be affected by any geological or extrinsic influences, and even within the same geological factor, the soil depression impact factor can be determined by different physical properties. As a result of reviewing a large number of papers and case histories, it can be seen that there are seven categories of ground subsidence factors. The depth and thickness of the overburden can affect the subsidence depending on the existence of the cavity, whereas the depth and orientation of the boundary between soil and rock are dominant factors in the ground composed of soil and rock. In case of soil layers, more various influencing factors exist such as type of soil, shear strength, relative density and degree of compaction, dry unit weight, water content, and liquid limit. The type of rock, distance from the main fracture and RQD can be influential factors in the bedrock. When approaching from the hydrogeological point of view, the rainfall intensity, the distance and the depth from the main channel, the coefficient of permeability and fluctuation of ground water level can influence to ground subsidence. It is also possible that the ground subsidence can be affected by external factors such as the depth of excavation and distance from the earth retaining wall, groundwater treatment methods at excavation work, and existence of artificial facilities such as sewer pipes. It is estimated that to evaluate the ground subsidence factor during the construction of underground structures in urban areas will be essential. It is expected that ground subsidence factors examined in this study will contribute for the reliable evaluation of the ground subsidence risk.

• Journal of Korean Society of Environmental Engineers
• /
• v.34 no.9
• /
• pp.597-603
• /
• 2012

To investigate the effects of weir construction on sediment characteristics of river bed, we conducted sediments sampling on the 9 locations near the weir, Kangchun, Yuju and Ipo in Namhan-River. Physical and chemical characteristics of sediments were analyzed by measuring particle size distribution, water content, Ignition loss, COD (Chemical Oxyzen Demand), TOC (Total Organic Carbon), TP (Total Phosphorus), SRP (Soluble Reactive Phosphorus) and TN (Total Nitrogen). Particle classification of all three weir sediments showed sandy loam that was caused by the river bed dredging. Due to the presence of weir, Ignition loss, COD, TOC, TP, SRP and TN showed similar trend such as the concentrations of upward weir had higher than those of downward weir. For the case of SRP concentration and C/N ratio, however, there is not much difference in the sediment characteristics compared to the those of sediments before weir construction. Therefore, It can be predicted that there are little effects of weir construction on sediment characteristics. However, weir construction could influence water quality of the river by controlling the transport and the accumulation of suspended materials from rainfall. Therefore, more intensive monitoring is required to examine the magnitude and patterns of sediment accumulation which could influence overlying water quality.

• Journal of Soil and Groundwater Environment
• /
• v.22 no.6
• /
• pp.74-84
• /
• 2017

Soil erosion is often extreme in Korea due to high rainfall intensities and steep slopes, and climate change has also increased the risk of erosion. Despite its significane, erosion-induced soil organic carbon (SOC) emission and water resource loss are not well understood, along with the lack of an integrated surface soil erosion protection policy. Therefore, to design adequate protection policies, land users, scientists, engineers and decision makers need proper information about surface soil and watershed properties related to greenhouse gas emission potential and water conservation capability, respectively. Assuming the total soil erosion of $346Tg\;yr^{-1}$, soil organic matter (SOM) content of 2% (58% of SOM is SOC), and mineralization rate of 20% of the displaced carbon, erosion-induced carbon emission could reach $800Gg\;C\;yr^{-1}$. Also the available water capacity of the soil was estimated to be 15.8 billion tons, which was 14 times higher than the yearly water supply demand in Seoul, Korea. Therefore, in order to prevent of soil erosion, this study proposes a three-stage plan for surface soil erosion prevention: 1) classification of soil erosion risk and scoring of surface soil quality, 2) selection of priority areas for conservation and best management practices (BMP), and 3) application of BMP and post management.

• Journal of Korea Water Resources Association
• /
• v.51 no.1
• /
• pp.71-80
• /
• 2018

For the stream management, time of concentration is one of the important factors. In particular, as the requirement about various application of the stream increased, accuracy assessment of concentration time in the stream as waterfront area is extremely important for securing evacuation at the flood. the past studies for the assessment of concentration time, however, were only performed on the single hydrological event in the complex basin of natural streams. The development of a assessment methods for the concentration time on the complex hydrological event in a single watershed of urban streams is insufficient. Therefore, we estimated the concentration time using the rainfall- runoff data for the past 10 years (2006~2015) for the Oncheon stream, the representative stream of the Busan, where frequent flood were taken place by heavy rains, in addition, reviewed the reliability using artificial neural network method based on Matlab. We classified a total of 254 rainfalls events based on over unrained 12 hours. Based on the classification, we estimated 6 parameters (total precipitation, total runoff, peak precipitation/ total precipitation, lag time, time of concentration) to utilize for the training and validation of artificial neural network model. Consequently, correlation of the parameter, which was utilized for the training and the input parameter for the predict and verification were 0.807 and 0.728, respectively. Based on the results, we predict that it can be utilized to estimate concentration time and analyze reliability of urban stream.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.6
• /
• pp.121-132
• /
• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.