• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 추계학술대회 논문집
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• pp.805-814
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• 2008

This study focused on the resilient modulus prediction model, which is the functions of mean effective principal stress and axial strain, for three types of railroad trackbed materials such as crushed stone, weathered soil, and crushed-rock soil mixture. The model is composed with the maximum Young's modulus and nonlinear values for higher strain in parallel with dynamic shear modulus. The maximum values is modeled by model parameters, $A_E$ and the power of mean effective principal stress, $n_E$. The nonlinear portion is represented by modified hyperbolic model, with the model parameters of reference strain, ${\varepsilon}_r$ and curvature coefficient, a. To assess the performance of the prediction models proposed herein, the elastic response of a test trackbed near PyeongTaek, Korea was evaluated using a 3-D nonlinear elastic computer program (GEOTRACK) and compared with measured elastic vertical displacement during the passages of freight and passenger trains. The material types of sub-ballasts are crushed stone and weathered granite soil, respectively. The calculated vertical displacements within the sub-ballasts are within the order of 0.6mm, and agree well with measured values with the reasonable margin. The prediction models are thus concluded to work properly in the preliminary investigation.

• 대기
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• 제26권1호
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• pp.35-45
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• 2016

Initialization of the global seasonal forecast system is as much important as the quality of the embedded climate model for the climate prediction in sub-seasonal time scale. Recent studies have emphasized the important role of soil moisture initialization, suggesting a significant increase in the prediction skill particularly in the mid-latitude land area where the influence of sea surface temperature in the tropics is less crucial and the potential predictability is supplemented by land-atmosphere interaction. This study developed a new soil moisture initialization method applicable to the KMA operational seasonal forecasting system. The method includes first the long-term integration of the offline land surface model driven by observed atmospheric forcing and precipitation. This soil moisture reanalysis is given for the initial state in the ensemble seasonal forecasts through a simple anomaly initialization technique to avoid the simulation drift caused by the systematic model bias. To evaluate the impact of the soil moisture initialization, two sets of long-term, 10-member ensemble experiment runs have been conducted for 1996~2009. As a result, the soil moisture initialization improves the prediction skill of surface air temperature significantly at the zero to one month forecast lead (up to ~60 days forecast lead), although the skill increase in precipitation is less significant. This study suggests that improvements of the prediction in the sub-seasonal timescale require the improvement in the quality of initial data as well as the adequate treatment of the model systematic bias.

• 한국지하수토양환경학회지:지하수토양환경
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• 제26권6호
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• pp.106-117
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• 2021

The purpose of this study is prediction of landslide occurrence reflecting the subsurface flow characteristics within the soil layer in the future due to climate change in a large scale watershed. To do this, we considered the infinite slope stability theory to evaluate the landslide occurrence with predicted soil moisture content by SWAT model based on monitored data (rainfall-soil moisture-discharge). The correlation between the SWAT model and the monitoring data was performed using the coefficient of determination (R²) and the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and, an accuracy analysis of landslide prediction was performed using auROC (area under Receiver Operating Curve) analysis. In results comparing with the calculated discharge-soil moisture content by SWAT model vs. actual observation data, R² was 0.9 and NSE was 0.91 in discharge and, R² was 0.7 and NSE was 0.79 in soil moisture, respectively. As a result of performing infinite slope stability analysis in the area where landslides occurred in the past based on simulated data (SWAT analysis result of 0.7~0.8), AuROC showed 0.98, indicating that the suggested prediction method was resonable. Based on this, as a result of predicting the characteristics of landslide occurrence by 2050 using climate change scenario (RCP 8.5) data, it was calculated that four landslides could occur with a soil moisture content of more than 75% and rainfall over 250 mm/day during simulation. Although this study needs to be evaluated in various regions because of a case study, it was possible to determine the possibility of prediction through modeling of subsurface flow mechanism, one of the most important attributes in landslide occurrence.

• 한국농공학회논문집
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• 제64권1호
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• pp.79-89
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• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2002년도 학술발표회 발표논문집
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• pp.85-88
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• 2002

This paper presents the result of the Water Erosion Prediction Project(WEPP) watershed scale model's application for prediction of sediment yield from a watershed which is comprised of hillslopes and channels and analyses of the soil loss from hillslopes and channels with crop practice and shape. To evaluate the model's application, the model is applied to a watershed that comprised of six hillslope and one channel, and the result was a good agreement with the observed values. The soil loss from hillslope was increased as the hills lope was under fallow conditions and slope length was longer. The soil loss from the channel was increased at the downstream for the concentration of flow.

• 한국농공학회지
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• 제37권1호
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• pp.81-89
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• 1995

The aim of the work described in this paper is to develope a constitutive model for the prediction of an unsaturated Soil and to confirm the application of the model, which is composed of the elastic and plastic part in consideration of the matric suction and the net mean stress. From test results, volume changes and deviator stresses are analyzed at each state and their relationships are formulated. And the application of the model to silty sands is con- firmed by the comparison between test and predicted results. During drying-wetting and loading-unloading processes for isotropic states, the agreement between predicted and test results are satisfactory. And predicted deviator stresses are well agreed with test results in shearing process. Overall acceptable predictions are reproduced in high confining pressure. Usefulness of the model is confirmed for the unsat- urated soil except volumetric strain, which is not well agreed with the test results due to deficiency of dilatancy of the model in low confining pressure. It is, therefore, recom- mended to study the behavior of dilatancy for an unsaturated soil.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• 한국농공학회:학술대회논문집
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• 한국농공학회 1999년도 Proceedings of the 1999 Annual Conference The Korean Society of Agricutural Engineers
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• pp.562-568
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• 1999

The model using homogenization technique based on energy concept for the prediction of the failure criterion of staple fiber mixed soil was developed to increase the practice and the application of staple fiber as a reinforcement for improving soft ground and agrictural structures. Parameters of the model are aspect ration and volumetric ocntnet of fiber, cohesion and internal friction angle of soil, adhesiion intercept of soil and fiber. It is judged that the model developed in this study is applicable to the soil composed of clay, silt and sand mixed by linear types of fiber such as steel bar, steel fiber , natural fiber etc..

• 한국농공학회지
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• 제42권2호
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• pp.86-92
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• 2000

The model using homogenization technique based on energy concept for the prediction of the failure criterion of staple fiber mixed soil was developed to increase the practice and the application of staple fiber as a reinforcement for improving soft ground. Parameters of the model are aspect ratio and volumetric content of fiber, cohesion and internal friction angle of soil, adhesion intercept and interface friction angle of soil and fiber. It is considered that the model developed in this study is applicable to the soil composed of clay, silt and sand mixed by thread types of fiber such as steel bar, steel fiber, natural fiber etc.

• Journal of Biosystems Engineering
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• 제30권6호통권113호
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• pp.360-365
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• 2005

Precision agriculture attempts to improve cropping efficiency by variable application of crop treatments such as fertilizers and pesticides, within field on a point-by-point basis. Therefore, a more complete understanding of the relationships between yield and soil properties is of critical importance in precision agriculture. In this study, the functional relationships between measured soil properties and rice yield were investigated. A supervised back-propagation neural network model was employed to relate soil chemical properties and rice yields on a point-by point basis, within individual site-years. As a results, a positive correlation was found between practical yields and predicted yields in 1999, 2000, 2001, and 2002 are 0.916, 0.879, 0.800 and 0.789, respectively. The results showed that significant overfitting for yields with only the soil chemical properties occurred so that more of environmental factors, such as climatological data, variety, cultivation method etc., would be required to predict the yield more accurately.