• Geomechanics and Engineering
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• v.39 no.2
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• pp.129-142
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• 2024

The most significant soil parameters that are utilized in geotechnical engineering projects' design and implementations are soil strength parameters including friction (ϕ), cohesion (c), and uniaxial compressive strength (UCS). Understanding soil shear strength parameters can be guaranteed the design success and stability of structures. In this regard, professionals always looking for ways to get more accurate estimations. The presented study attempted to investigate soil shear strength parameters by using multivariate regression and multilayer perceptron predictive models which were implemented on 100 specimens' data collected from the Tabriz region (NW of Iran). The uniaxial (UCS), liquid limit (LL), plasticity index (PI), density (γ), percentage of fine-grains (pass #200), and sand (pass #4) which are used as input parameters of analysis and shear strength parameters predictions. A confusion matrix was used to validate the testing and training data which is controlled by the coefficient of determination (R²), mean absolute (MAE), mean squared (MSE), and root mean square (RMSE) errors. The results of this study indicated that MLP is able to predict the soil shear strength parameters with an accuracy of about 93.00% and precision of about 93.5%. In the meantime, the estimated error rate is MAE = 2.0231, MSE = 2.0131, and RMSE = 2.2030. Additionally, R² is evaluated for predicted and measured values correlation for friction angle, cohesion, and UCS are 0.914, 0.975, and 0.964 in the training dataset which is considerable.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3B
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• pp.285-291
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• 2011

Accurate assessment of the water and energy cycles is essential to understand hydrologic, climatologic, and ecological processes. Common Land Model (CLM) is one of the well-developed Soil-Vegetation-Atmosphere Transfer (SVAT) models based on the water and energy balance equation for accurate prediction of hydro-environmental cycles. The CLM can estimate realistic and reliable results using relatively simple parameters. It has been widely used in the world, however in Korea practical applications of the CLM are rare due to lack of information and input data. In this study, the CLM with Korea Flux network (KoFlux) and Kore Land Data Assimilation System (KLDAS) data were individually validated for domestic applications. This study showed that all comparisons between observations and model results from KoFlux and KLDAS had reasonable correlation with determination coefficient of 0.73~1.00 via regression. The results confirmed the applicability of the CLM and the possibility of the KLDAS usage for the region where input data are not existed.

• Journal of Ecology and Environment
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• v.32 no.1
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• pp.47-53
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• 2009

The soil carbon stock for a Pinus densiflora forest at Gwangneung, central Korea was estimated using the soil carbon model, Yasso. The soil carbon stock measured in the forest was 43.73 t C $ha^{-1}$, and the simulated initial (steady state) soil carbon stock and the simulated current soil carbon stock in 2007 were 39.19 t C $ha^{-1}$ and 38.90 t C $ha^{-1}$, respectively. Under the assumption of a $0.1^{\circ}C$ increase in mean annual temperature per year, the decomposition and litter fractionation rates increased from 0.28 to 0.56 % $year^{-1}$ and the soil carbon stock decreased from 0.03 to 0.12 % $year^{-1}$. Yasso is a simple and general model that can be applied in cases where there is insufficient input information. However, in order to obtain more accurate estimates in Korea, parameters need to be recalibrated under Korean climatic and vegetation conditions. In addition, the Yasso model needs to be linked to other models to generate better litter input data.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.89-99
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• 2019

In this study, we estimated the spatially-distributed soil moisture at the high resolution ($10m{\times}10m$) using the satellite-based Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images. The Sentinel-1A/B raw data were pre-processed using the SNAP (Sentinel Application Platform) tool provided from ESA (European Space Agency), and then the pre-processed data were converted to the backscatter coefficients. The regression equations were derived based on the relationships between the TDR (Time Domain Reflectometry)-based soil moisture measurements and the converted backscatter coefficients. The TDR measurements from the 51 RDA (Rural Development Administration) monitoring sites were used to derive the regression equations. Then, the soil moisture values were estimated using the derived regression equations with the input data of Sentinel-1A/B based backscatter coefficients. Overall, the soil moisture estimates showed the linear trends compared to the TDR measurements with the high Pearson's correlations (more than 0.7). The Sentinel-1A/B based soil moisture values matched well with the TDR measurements with various land surface conditions (bare soil, crop, forest, and urban), especially for bare soil (R: 0.885~0.910 and RMSE: 3.162~4.609). However, the Mandae-ri (forest) and Taean-eup (urban) sites showed the negative correlations with the TDR measurements. These uncertainties might be due to limitations of soil surface penetration depths of SAR sensors and complicated land surface conditions (artificial constructions near the TDR site) at urban regions. These results may infer that qualities of Sentinel-1A/B based soil moisture products are dependent on land surface conditions. Although uncertainties exist, the Sentinel-1A/B based high-resolution soil moisture products could be useful in various areas (hydrology, agriculture, drought, flood, wild fire, etc.).

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.04a
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• pp.91-99
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• 2001

This paper is for a reasonable selection of design response spectra for the seismic design of specific types of soil-structure interaction systems, e.g., underground structure within flexible soil profiles of structures on the shallow soil layers on the stiff bed rock. the existing backup data used for determining the design response spectra of the Code have been investigated and evaluated. For this purpose, various types of free field analyses have been performed using one-dimensional wave propagation theory considering the nonlinear properties of the soil profile. As a result, a reasonable approach of determining input response spectra for specific soil profiles has been proposed to be compatible to the design response spectra of the Code.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.3
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• pp.205-212
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• 2014

Chronological changes of soil organic carbon (SOC) must be prepared by IPCC guidelines for national greenhouse gas inventories. IPCC suggested default reference SOC stocks for mineral soils and relative stock factors for different management activities where country own factors were not prepared. 3.4 million data were downloaded from agricultural soil information system and analyzed to get chronological changes of SOC for some counties and for land use in Korea. SOC content of orchard soil was higher than the other soils but chronological SOC changes of all land use had no tendency in differences with high standard deviation. SOC contents of counties depended on their own management activities and chronological SOC changes of districts also had no tendency in differences. Thus, Korea should survey the official records and relative stock factors on management activities such as land use, tillage and input of organic matter to calculate SOC stocks correctly. Otherwise, Korea should establish a model for predicting SOC by analyzing selected representative fields and by calculating SOC differences from comparing management activities of lands with those of representative fields.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.39-44
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• 2001

A GIS-RUSLE system has been developed to estimate soil losses from where individual fields on a hillslope using the RUSLE in a GIS environment. The GIS-RUSLE adopts a user interface and a user can use the drop-down menus to define the inputs and get the calculated results. The input data are generated from Arc/Info and ArcView GIS and the factors of the RUSLE are provided from ArcView Avenue. Examples of the GIS-RUSLE applications are presented.

• Geomechanics and Engineering
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• v.31 no.1
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• pp.113-127
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• 2022

The undrained shear strength of soil is considered one of the engineering parameters of utmost significance in geotechnical design methods. In-situ experiments like cone penetration tests (CPT) have been used in the last several years to estimate the undrained shear strength depending on the characteristics of the soil. Nevertheless, the majority of these techniques rely on correlation presumptions, which may lead to uneven accuracy. This research's general aim is to extend a new united soft computing model, which is a combination of random forest (RF) with grasshopper optimization algorithm (GOA) to the pile set-up parameters' better approximation from CPT, based on two different types of data as inputs. Data type 1 contains pile parameters, and data type 2 consists of soil properties. The contribution of this article is that hybrid GOA - RF for the first time, was suggested to forecast the pile set-up parameter from CPT. In order to do this, CPT data and related bore log data were gathered from 70 various locations across Louisiana. With an R² greater than 0.9098, which denotes the permissible relationship between measured and anticipated values, the results demonstrated that both models perform well in forecasting the set-up parameter. It is comprehensible that, in the training and testing step, the model with data type 2 has finer capability than the model using data type 1, with R² and RMSE are 0.9272 and 0.0305 for the training step and 0.9182 and 0.0415 for the testing step. All in all, the models' results depict that the A parameter could be forecasted with adequate precision from the CPT data with the usage of hybrid GOA - RF models. However, the RF model with soil features as input parameters results in a finer commentary of pile set-up parameters.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.4
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• pp.181-192
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• 2003

We developed a mathematical simulation model to portray the vertical distribution of soil water from the measured weather data and the known soil hydraulic properties, and then compared simulation results with the periodically measured soil water profiles obtained on Jungdong sandy loam to verify the model, In this model, we solved potential-based Richards' equation by the implicit finite difference method superimposed on the predictor-corrector scheme. We presumed that: soil hydraulic properties are homogeneous; soil water flows isothermally; hysteresis is not considered; no vapor flows; no heat transfers into the soil profiles; and water added to soil surface is distributed along the soil profile following partial displacement principle. The input data were broadly classified into two groups: (1) daily weather data such as rainfall, maximum and minimum air temperatures, relative humidity and solar radiation and (2) soil hydraulic data to approximate unsaturated hydraulic conductivity and water retention. Each hydraulic polynomial function approximated using the Chebyshev polynomial and least square difference technique in tandem showed a fairly good fit of the given set of data. Vertical distribution of soil water as approximations to the Richards' equation subject to changing surface and phreatic boundaries was solved numerically during 53 days with a comparatively large time increment, and this pattern agreed well with field neutron scattering data, except for the surface 0.1 m slab.

• Korean Journal of Remote Sensing
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• v.2 no.2
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• pp.117-131
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• 1986

The purpose of this study was to develop techniques acquiring hydrologic parameters that affect runoff conditions from Landsat imagery. Runoff conditions in a study area were analyzed by employing the U.S. Soil Conservation Service(SCS) Method. SCS runoff curve numbers(CN) were estimated by the computer analysis of Landsat imagery and digiral terrain model(DTM) data. The SCS Method requires land use/cover and soil conditions of the area as input parameters. A land use/cover map of 5 hydrological classes was produced from the Landsat multi-spectral scannerr imagery. Slope-gradient and contour and contour maps were also made using the DTM topographic data. Inundation areas depending on reservoir levels were able to be mapped on the Landsat scene by combining the contour data.