• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1195-1205
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• 2018

In the study, the water shortage of Boryeong Dam watershed ($163.6km^2$) was evaluated under future climate change scenario. The Soil and Water Assessment Tool (SWAT) was used considering future dam release derived from multiple linear regression (MLR) analysis. The SWAT was calibrated and verified by using daily observed dam inflow and storage for 12 years (2005 to 2016) with average Nash-Sutcliffe efficiency of 0.59 and 0.91 respectively. The monthly dam release by 12 years MLR showed coefficient of determination ($R^2$) of above 0.57. Among the 27 RCP 4.5 scenarios and 26 RCP 8.5 scenarios of GCM (General Circulation Model), the RCP 8.5 BCC-CSM1-1-M scenario was selected as future extreme drought scenario by analyzing SPI severity, duration, and the longest dry period. The scenario showed -23.6% change of yearly dam storage, and big changes of -34.0% and -24.1% for spring and winter dam storage during 2037~2047 period comparing with 2007~2016 period. Based on Runs theory of analyzing severity and magnitude, the future frequency of 5 to 10 years increased from 3 in 2007~2016 to 5 in 2037~2046 period. When considering the future shortened water shortage return period and the big decreases of winter and spring dam storage, a new dam operation rule from autumn is necessary for future possible water shortage condition.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.481-491
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• 2024

Recent droughts make hydroelectric power generation (HPG) decreasing. Due to climate change in the future, the frequency and intensity of drought are expected to increase, which will increase uncertainty of HPG in multi-purpose dams. Therefore, it is necessary to estimate the amount of HPG according to climate change scenarios and analyze the effect of drought on the amount of HPG. This study analyzed the future HPG of the Soyanggang Dam and Chungju Dam according to the SSP2-4.5 and SSP5-8.5 scenarios. Regression equations for HPG were developed based on the observed data of power generation discharge and HPG in the past provided by My Water, and future HPGs were estimated according to the SSP scenarios. The effect of drought on the amount of HPG was investigated based on the drought severity calculated using the standardized precipitation index (SPI). In this study, the future SPIs were calculated using precipitation data based on four GCM models (CanESM5, ACCESS-ESM1-5, INM-CM4-8, IPSL-CM6A) provided through the environmental big data platform. Overall results show that climate change had significant effects on the amount of HPG. In the case of Soyanggang Dam, the amount of HPG decreased in the SSP2-4.5 and SSP5-8.5 scenarios. Under the SSP2-4.5 scenario the CanESM model showed a 65% reduction in 2031, and under the SSP5-8.5 scenario the ACCESS-ESM1-5 model showed a 54% reduction in 2029. In the case of Chungju Dam, under the SSP2-4.5 and SSP5-8.5 scenarios the average monthly HPG compared to the reference period showed a decreasing trend except for INM-CM4 model.

• Economic and Environmental Geology
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• v.50 no.2
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• pp.129-143
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• 2017

The shallow sediments in the southwestern Ulleung Basin consist of mass flow deposits such as slide/slump and debris flow deposits (DFD), caused by slope failure. These sediments are proven to be important in studying geological disaster and stability of the seafloor. In this paper, we analysised the flow accumulation and slope failure susceptibility of the Ulleung Basin on the basis of multi-beam data, collected in this area. We also studied the distribution pattern and the seismic characteristics of the DFD in the uppermost layer of the Ulleung Basin on the basis of seismic data. The slope susceptibility was calculated as the frequency ratio of each factors including slope, aspect, curvature and stream power index (SPI), which causes the slope failure. These results indicate that the slope failure is frequently to occur in the southern and western continental slope of the Ulleung Basin. The sediment flow (mass flow) caused by the slope failure converges to the north and northwest of the Ulleung Basin. According to the seismic characteristics, the uppermost layer in study area can be divided into four sedimentary unit. These sedimentary units develop from the south and southwest to the north and northwest in association with slope susceptibility and flow accumulation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.242-252
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• 2016

A process-based ecosystem model, BIOME-BGC, was applied to simulate seasonal and inter-annual dynamics of carbon and water processes for potential evergreen needleleaf forest (ENF) biome in Korea. Two simulation sites, Milyang and Unljin, were selected to reflect warm-and-dry and cool-and-wet climate regimes, where massive diebacks of pines including Pinus densiflora, P. koraiensis and P thunbergii, were observed in 2009 and 2014, respectively. Standard Precipitation Index (SPI) showed periodic drought occurrence at every 5 years or so for both sites. Since mid-2000s, droughts occurred with hotter climate condition. Among many model variables, Cpool (i.e., a temporary carbon pool reserving photosynthetic compounds before allocations for new tissue production) was identified as a useful proxy variable of tree carbon starvation caused by reduction of gross primary production (GPP) and/or increase of maintenance respiration (Rm). Temporal Cpool variation agreed well with timings of pine tree diebacks for both sites. Though water stress was important, winter- and spring-time warmer temperature also played critical roles in reduction of Cpool, especially for the cool-and-wet Uljin. Shallow soil depth intensified the drought effect, which was, however, marginal for soil depth shallower than 0.5 m. Our modeling analysis implicates seasonal drought and warmer climate can intensify vulnerability of ENF dieback in Korea, especially for shallower soils, in which multi-year continued stress is of concern more than short-term episodic stress.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.327-335
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• 2020

The method of selecting an existing flood hazard area via a numerical model requires considerable time and effort. In this regard, this study proposes a method for selecting flood vulnerable areas through topographic analysis based on a surface runoff mechanism to reduce the time and effort required. Flood vulnerable areas based on runoff mechanisms refer to those areas that are advantageous in terms of the flow accumulation characteristics of rainfall-runoff water at the surface, and they generally include lowlands, mild slopes, and rivers. For the analysis, a digital topographic map of the target area (Seoul) was employed. In addition, in the topographic analysis, eight topographic factors were considered, namely, the elevation, slope, profile and plan curvature, topographic wetness index (TWI), stream power index, and the distances from rivers and manholes. Moreover, receiver operating characteristic analysis was conducted between the topographic factors and actual inundation trace data. The results revealed that four topographic factors, namely, elevation, slope, TWI, and distance from manholes, explained the flooded area well. Thus, when a flood vulnerable area is selected, the prioritization method for various factors as proposed in this study can simplify the topographical analytical factors that contribute to flooding.