• Geotechnical Engineering
• /
• v.8 no.2
• /
• pp.5-20
• /
• 1992

The physical-based and lumped-parameter hydrologic groundwater flow model for predicting the rainfall-triggered rise of groundwater levels in hillside slopes is developed in this paper to assess the risk of landslides. The developed model consists of a vertical infiltration model for unsaturated zone linked to a linear storage reservoir model(LSRM) for saturated zone. The groundwater flow model has uncertain constants like soil depttL slope angle, saturated permeability, and potential evapotranspiration and four free model parameters like a, b, c, and K. The free model parameters could be estimated from known input-output records. The BARD algorithm is uses as the parameter estimation technique which is based on a linearization of the proposed model by Gauss -Newton method and Taylor series expansion. The application to examine the capacity of prediction shows that the developed model has a potential of use in forecast systems of predicting landslides and that the optimal estimate of potential 'a' in infiltration model is the most important in the global optimum analysis because small variation of it results in the large change of the objective function, the sum of squares of deviations of the observed and computed groundwater levels. 본 논문에서는 가파른 산사면에서 산사태의 발생을 예측하기 위한 수문학적 인 지하수 흐름 모델을 개발하였다. 이 모델은 물리적인 개념에 기본하였으며, Lumped-parameter를 이용하였다. 개발된 지하수 흐름 모델은 두 모델을 조합하여 구성되어 있으며, 비포화대 흐름을 위해서는 수정된 abcd 모델을, 포화대 흐름에 대해서는 시간 지체 효과를 고려할 수 있는 선형 저수지 모델을 이용하였다. 지하수 흐름 모델은 토층의 두께, 산사면의 경사각, 포화투수계수, 잠재 증발산 량과 같은 불확실한 상수들과 a, b, c, 그리고 K와 같은 자유모델변수들을 가진다. 자유모델변수들은 유입-유출 자료들로부터 평가할 수 있으며, 이를 위해서 본 논문에서는 Gauss-Newton 방법을 이용한 Bard 알고리즘을 사용하였다. 서울 구로구 시흥동 산사태 발생 지역의 산사면에 대하여 개발된 모델을 적용하여 예제 해석을 수행함으로써, 지하수 흐름 모델이 산사태 발생 예측을 위하여 이용할 수 있음을 입증하였다. 또한, 매개변수분석 연구를 통하여, 변수 a값은 작은 변화에 대하여 목적함수값에 큰 변화를 일으키므로 a의 값에 대한 최적값을 구하는 것이 가장 중요한 요소라는 결론을 얻었다.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.627-646
• /
• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Tunnel and Underground Space
• /
• v.22 no.4
• /
• pp.266-275
• /
• 2012

In this study, a method is devised to implement a supercritical $CO_2$ ($scCO_2$) injection environment on a laboratory scale and to investigate the effects of $scCO_2$ on the properties of rock specimens. Specimens of shale and sandstone normally constituting the cap rock and reservoir rock, respectively, were kept in a laboratory reactor chamber with $scCO_2$ for two weeks. From this stage, a chemical reaction between rock surface and the $scCO_2$ was induced. The effect of saline water was also investigated by comparing three conditions ($scCO_2$-rock, $scCO_2-H_2O$-rock and $scCO_2$-brine(1M)-rock). Finally, we checked the changes in the properties before and after the reaction by destructive and nondestructive testing procedures. The swelling of shale was a main concern in this case. The experimental results suggested that $scCO_2$ has a greater effect on the swelling of the shale than pure water and brine. It was also observed that the largest swelling displacement of shale occurred after a reaction with the $H_2O-scCO_2$ solution. The results of a series of the destructive and nondestructive tests indicate that although each of the property changes of the rock differed depending on the reaction conditions, the $H_2O-scCO_2$ solution had the greatest effect. In this study, shale was highly sensitive to the reaction conditions. These results provide fundamental information pertaining to the stability of $CO_2$ storage sites due to physical and chemical reactions between the rocks in these sites and $scCO_2$.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1341-1352
• /
• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2001.04a
• /
• pp.98-103
• /
• 2001

In order to understand the characteristics of leachate at the Seokdae municipal waste landfill in the Pusan city, the correlation between leachate pollution loading and volume of gas production. concentration of gas and subsidence of ground, the characteristical methos, geochemical analyses and laboratory column tests using samples of gases, leachate and surface soil of Seokdae waste landfill area. Through the analysis of water balance, leachate flow rate and pollution loading were estimated. Geistatistical analysis of four gas components ( $O_2$, C $H_4$, $H_2$S and CO) shows the possibility of ground subsidence around the group of a site with high concentration of gas. From geochemical analyses of leachate, EC and Total-Alkalinity of ground subsidence around the group of a site with high concentration of gas. From geochemical analysis of leachate, Ec and Total-Alkalinity were increased, and Cl, Cr, Mn, Cu, Zn, Cd and Pb were decreassed comparing to the part, and the type of water quality was Na-HC $O_3$ in trilinear diagram. It shows that biodecomposition of municipal wastes continues actively. From the analysis of water balance, the total leachate flow rate is about 465.11㎥/day and pure pollution loading of Cl, Mn and Fe are estimated to 223.8kg/day, 0.2kg/day, 0.3kg/day, respectively. The laboratory column test of residual soil and landfill soil shows 0.206cm and 0.019cm for linear velocity(equation omitted), 0.234 $\textrm{cm}^2$/min and 0.018$\textrm{cm}^2$/min for diffusion coefficient ( $D_{ι}$), and 1.136cm and 0.095cm longitudinal dispersion index ($\alpha$$_{ι}$), respective]y. It demonstrates that the delay time of contamination for residual soil is shorter than that of landfill soil.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.29 no.2
• /
• pp.109-120
• /
• 2017

A discounted cost model for preventive maintenance of armor units of rubble-mound breakwaters is mathematically derived by combining the deterioration model based on a discrete-time stochastic process of shock occurrence with the cost model of renewal process together. The discounted cost model of condition-based maintenance proposed in this paper can take into account the nonlinearity of cumulative damage process as well as the discounting effect of cost. By comparing the present results with the previous other results, the verification is carried out satisfactorily. In addition, it is known from the sensitivity analysis on variables related to the model that the more often preventive maintenance should be implemented, the more crucial the level of importance of system is. However, the tendency is shown in reverse as the interest rate is increased. Meanwhile, the present model has been applied to the armor units of rubble-mound breakwaters. The parameters of damage intensity function have been estimated through the time-dependent prediction of the expected cumulative damage level obtained from the sample path method. In particular, it is confirmed that the shock occurrences can be considered to be a discrete-time stochastic process by investigating the effects of uncertainty of the shock occurrences on the expected cumulative damage level with homogeneous Poisson process and doubly stochastic Poisson process that are the continuous-time stochastic processes. It can be also seen that the stochastic process of cumulative damage would depend directly on the design conditions, thus the preventive maintenance would be varied due to those. Finally, the optimal periods and scale for the preventive maintenance of armor units of rubble-mound breakwaters can be quantitatively determined with the failure limits, the levels of importance of structure, and the interest rates.