• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.361-364
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• 2005

The use of remotely sensed data and geographic information system (GIS) to develop conservation-oriented watershed management strategies on Imha Dam, Korea, is presented. The change of land use for study area was analyzed using multi-temporal Landsat imagery. A soil loss model was executed within a GIS environment to evaluate watershed management strategies in terms of soil loss. In general, remotely sensed data provide efficient means of generating the input data required for the soil loss model. Also, GIS allowed for easy assessment of the relative erosion hazard over the watershed under the different land use change options. The soil loss model predicted substantial declines in soil loss under conservation-oriented land management compared to current land management for Imha Dam. The results of this study indicate that soil loss potential (5,782,829 ton/yr) on Imha Dam in 2003 is approximately 1.27 times higher than that (4,557,151 ton/yr) in 1989. This study represents the first attempt in the application of GIS technology to watershed conservation planning for Imha Dam. The procedures developed will contribute to the evolution of a decision support system to guide the land planning and dam management in Imha Dam.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.368-368
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• 2022

The Soil and Water Assessment Tool (SWAT) has been widely used to simulate the long-term hydrological conditions of a catchment. Two output variables, outflow and sediment yield have been widely investigated in the field of water resources management, especially in determining the conditions of ungauged subbasins. The presence of missing data in weather input data can cause poor representation of the climate conditions in a catchment especially for large or mountainous catchments. Therefore, in this study, a custom module was developed and evaluated to determine the efficiency of utilizing basic spatial interpolation methods in the estimation of weather input data. The module has been written in Python language and can be considered as a pre-processing module prior to using the SWAT model. The results of this study suggests that the utilization of the proposed pre-processing module can improve the simulation results for both outflow and sediment yield in a catchment, even in the presence of missing data.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.154-154
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• 2019

Characterization of reliable field-scale root-zone soil moisture (RZSM) variability contribute to effective hydro-meterological monitoring. Although a promising cosmic-ray neutron probe (CRNP) holds the pontential for field-scale RZSM measurement, it is often restricted at deeper depths due to the non-unique sensitivity of CRNP-measured fast neutron signal to other hydrogen pools. In this study, a merging framework relied on coupling cosmic-ray soil moisture with a representative additional RZSM, was introduced to scale shallower CRNP effective depth to represent root-zone layer. We tested our proposed framework over a densely vegetated region in South Korea covering a network of one CRNP and nine in-situ point measurements. In particular, cosmic-ray soil moisture and ancillary RZSM retrieved from the most time stable location were considered as input datasets; whereas the remaining point locations were used to generate a reference RZSM product. The errors between these two input datasets and the reference were forecasted by a linear autoregressive model. A linear combination of forecasts was then employed to compute a suitable weight for merging two input products from the predicted errors. The performance of merging framework was evaluated against reference RZSM in comparison to the two original products and a commonly used exponential filter technique. The results of this study showed that merging framework outperformed other products, demonstrating its robustness in improving field-scale RZSM. Moreover, a strong relationship between the quality of input data and the performance merging framework in light of CRNP effective depth variation has been also underlined via the merging framework.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.439-439
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• 2015

The temporal and spatial relationship of the weather elements such as rainfall and temperature is closely linked to the streamflow simulation, especially, to the flood forecasting problems. For the study area, Imjin river basin, which has the specific characteristics in geography with river cross operation between North and South Korea, the meteorological information in the northern area is totally deficiency, lead to the inaccuracy of streamflow estimation. In the paper, this problem is solved by using the combination of global (such as soil moisture content, land use) and local hydrologic components data such as weather data (precipitation, evapotranspiration, humidity, etc.) for the model-driven runoff (surface flow, lateral flow and groundwater flow) data in each subbasin. To compute the streamflow in Imjin river basin, this study is applied the hydrologic model SURR (Sejong Univ. Rainfall-Runoff) which is the continuous rainfall-runoff model used physical foundations, originally based on Storage Function Model (SFM) to simulate the intercourse of the soil properties, weather factors and flow value. The result indicates the spatial variation in the runoff response of the different subbasins influenced by the input data. The dependancy of runoff simulation accuracy depending on the qualities of input data and model parameters is suggested in this study. The southern region with the dense of gauges and the adequate data shows the good results of the simulated discharge. Eventually, the application of SURR model in Imjin riverbasin gives the accurate consequence in simulation, and become the subsequent runoff for prediction in the future process.

• Journal of Korean Society on Water Environment
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• v.22 no.4
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• pp.619-625
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• 2006

Amount of soil loss is important information for the proper water quality management, In this research, annual average soil loss of the Geum River basin was estimated using RUSLE (Revised Universal Soil Loss Equation) and GIS (Geographic Information System). Input data were manipulated using ArcGIS ver. 8.3. From crop field which constitute 8.2% of the Geum River Basin, annual average soil loss was estimated as 53.6 ton/ha/year. From the rice paddy field which constitutes 20% of the Geum River Basin, soil loss was estimated as 33.5 ton/ha/year, In comparison, forestry area which constitutes 61.8% of the basin discharged 2.8 ton/ha/year, It could be known from this research that appropriate measures should be implemented to prevent excessive soil loss from the agricultural areas.

• Tunnel and Underground Space
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• v.5 no.4
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• pp.336-346
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• 1995

Geotechnical Information System (GTIS) for efficient management of three dimensional borehole data has been developed. Some problems were raised during the input process of borehole data, and alternative solutions were sought. According to the previous geotechnical reports, there is no unified weathering classification scheme. A criterion, 100 times/30cm from SPT, was turned out inappropriate to the discrimination of weathered rock from weathered soil. It has also been suggested that weathered soil, weathered rock, soft rock, and hard rock should be defined as CW, HW, MW, and SW~fresh condition. For better comparison of RQD, the use of NX size coring is recommended for the whole area although BX size coring has been used in excavated area. The limit of drilling depth up to 1 m from the top of surface of hard rock should be extended to avoid possible wrong interpretation of rock head due to the existence of corestone. The input data were analysed by geostatistical methods. It is found that the range in semivariogram is about 300m, and the variance of gneiss is greater than that of granite. It is because the granite data analysed came from almost single uniform rock mass(i.e.Seoul granite), but gneiss data came from the rock mass(i.e. Gyeonggi gneiss complex experienced several metamorphic metamorphic processes.

• Economic and Environmental Geology
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• v.25 no.2
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• pp.145-151
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• 1992

In order to study the optimum depth for the soil geochemical exploration in the area which is affected by agricultural activities and waste disposal of metal mine, the soil samples were sampled from the B layer of residual soil and vertical 7 layers up to 250 cm in the rice field and 3 layers up to 90 cm in the ordinary field. They were analyzed for Au, As, Cu, Pb and Zn by AAS, AAS-graphite furnace and ICP. To investigate the proper depth for the soil sampling in the contaminated area, the data were treated statistically by applying correlation coefficient, factor analysis and trend analysis. It is conclude that soil geochemical exploration method could be applied in the farm-land and a little contaminated area. The optimum depth of soil sampling is 60 cm in the ordinary field, and 150~200 cm in the rice field. Soil sampling in the area of a huge mine waste disposal is not recommendable. Plotting of geochemical map with factor scores as a input data shows a clear pattern compared with the map of indicater element such as As or Au. The second or third degree trend surface analysis is effective in inferring the continuity of vein in the area where the outcrop is invisible.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.695-703
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• 2013

Soil erosion and sediment delivery ratio(SDR) were estimated by using HSPF model in 3 tributaries of upper stream of Geum river-basin. Meteorological data and other input data were constructed from 2006 to 2011 year by the HSPF model. Flow and suspended solid results were relatively matched with the measurement data through the calibration and validation of the model. Soil erosion was proportional to the amount of rainfall and the area of watershed based on the results of model calibration and validation. SDR in Moojunamdea stream was the highest and one in Cho stream was the lowest. This was effected by the geographical characteristic. SDR was 17.6% Moojunamdea stream, 9.1% Cho stream and 13.2 % Bocheong stream. As the SDR was effected by watershed area and shape factor in this study area.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.