• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.97-104
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• 2009

GIS (Geographic Information System) is very useful in describing basin wide geographic characteristics and hydrologic analysis. This study estimated long term hydrologic variations in the Geum river basin using the SSARR rainfall runoff simulation model to provide reliable hydrologic information associated with rainfall runoff management module. Calibrated various hydrologic information such as soil moisture index, water use, direct and base flow are generated using GIS tools to display spatial hydrologic information in the unit of subbasin of target watershed. In addition, the graphic user interface toolkit designed for data compilation is expected to support efficient basin wide rainfall runoff analysis.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.130-145
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• 2016

Soil erosion processes are affected by weather factors, such as rainfall, temperature, wind, and humidity. Among these factors, rainfall directly influences soil erosion by breaking away soil particles. The kinetic energy of rainfall and water flow caused by rain entrains and transports soil particles downstream. Therefore, in order to estimate soil erosion, it is important to accurately determine the rainfall erosivity factor(R) in RUSLE(Revised Universal Soil Loss Equation). The objective of this study is to evaluate the average annual R using 14 years(2002~2015) of 1 minute rainfall data from 55 KMA(Korea Meteorological Administration) weather stations. The R results from 1 min rainfall were compared with previous R studies using 1 h rainfall data. The determination coefficients($R^2$) between R calculated using 1 min rainfall data and annual rainfall were 0.70-0.98. The estimation of 30 min rainfall intensity from 1 min rainfall data showed better $R^2$ results than results from 1 h rainfall data. For estimation of physical spatial rain erosivity(R), distribution of annual rainfall was estimated by IDW(Inverse Distance Weights) interpolation, taking elevation into consideration. Because of the computation burden, the R calculation process was programmed using the python GUI(Graphical User Interface) tool.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.19-27
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• 2012

RUSLE(Revised Universal Soil Loss Equation) has been widely used to estimate the soil loss amount of watersheds from rainfall erosivity, soil erodibility, topographic features and cropping management condition. Rainfall erosivity is the most dominant and sensitive factor among these so that the determination of reliable rainfall erosivity is essential to estimate the soil loss of watershed. Since there has been no criterion to determine the rainfall erosivity in Korea, the empirical values, determined from the relation between the annual average rainfall and erosivity or suggested by TBR(Transport Research Board), have been used for designing the erosion control structure and controlling the soil erosion for watersheds. In this study, the procedure for estimating the rainfall erosivity using frequency analysis is proposed. The most fitted distribution function, with calculated rainfall erosivities with various frequencies and durations, has been also selected. The suggested procedure can be used to estimate the optimal value of rainfall erosivity for RUSLE in order to design soil erosion structures and control the soil erosion in watersheds effectively.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.125-135
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• 2019

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.687-696
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• 2006

Slope failure triggered by rainfall produces severe effects on the serviceability and stability of railway. Therefore slope stability problem is one of the major concerns on the operation of railway. In this study we collected rainfall data when and where slope failures were observed. The collected data show that the range of cumulative rainfall is from 150 to 500mm and the rainfall duration is about 3 to 24 hours. By using the collected rainfall information, slope stability analysis considering infiltration was carried. The analyses employs multiple sliding surfaces to find the minimal factor of safety in the infinite slope condition. This approach show more reasonable results than the results from analysis following the design code which assumes that groundwater level and the slope surface are equal.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1447-1456
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• 2017

Accurate inference of parameters of a stochastic rainfall generation model is essential to improve the applicability of the rainfall generation model which modeled the rainfall process and the structure of rainfall events. In this study, the model parameters of a stochastic rainfall generation model, NSRPM (Neyman-Scott rectangular pulse model), were estimated using DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, and DE (differential evolution) methods. Summer season hourly rainfall data of 20 rainfall observation sites within the Nakdong river basin from 1973 to 2017 were used to estimate parameters and the regional applicability of inference methods were analyzed. Overall results demonstrated that DE and Nelder-Mead methods generate better results than that of DFP and GA methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.804-806
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• 2003

Probable Maximum Precipitation (PMP) is essential in the design of hydraulic structures such as dams, weirs and flood control structures. Up to the present, PMP has been derived from any proper single storm which can have a large error. PMP values should be evaluated from many historic heavy storm events from all over the country. Since this can be done at the spots of storm occurring and the calculated PMP from all spots in the country can be correlated. The objectives of this study are therefore to evaluate PMP from historic heavy storm data from 1972 to 2000 by using meteorological method, then to correlate and to present the results using GIS. The maximized rainfall depths can be calculate from depth of heavy rainfall and dew point temperature, and then can be analyzed for each rainfall duration to obtain spatial rainfall distribution by using GIS. The depth-area-duration relationship of maximized rainfall can be obtained and this helps to develop enveloped curves . The results from this study are a set of contour maps of PMP for each rainfall duration for all over the country and the depth-area-duration relationships for the area of 100 to 50,000 km.$^{2}$ at duration of 1, 2 and 3 days.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.187-192
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• 2000

The severity of drought could be evaluated by the accumulative rainfall method, soil moisture condition method, storage ratio method, and water supply restriction intensity method, etc. The pattern of drought could be forecast with the most similar pattern of accumulative rainfall out of the file of past rainfall history. The information that how much rainfall should be expected to overcome the present drought could be obtained from the reservoir storage ratio and soil moisture condition.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.35-44
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• 2001

As one of advanced uses of radar, a physically based rainfall prediction method which uses a conceptual rainfall model assimilated by information from volume scanning radar is shown. As another example of advanced utilization of weather radar, results from analyzing a hierarchical time-scale structure in dependency of rainfall distribution en topography are shown.