• The Journal of Engineering Geology
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• v.16 no.2 s.48
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• pp.201-214
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• 2006

To study the relationship between rainfall conditions and landslides according to a geological condition in land-slides areas such asJangheung Kyounggi, Sangju and Pohang Kyoungbuk, the data of rainfall and landslides are investigated and analyzed. Many landslides occurred at these areas because of the heavy rainfall in two or four days of the summer 1998. The data of rainfall are collected in observatories within a 50km radius from landslides occurrence areas, and the data of landslides are investigated directly in landslides areas. The data of rainfall are the accumulative rainfall and the rainfall intensity, and the data of landslides are the occurrence frequency considering the geological condition. These data are analyzed statistically to know the relationship the rainfall and landslides. The landslides are concentrated in the heavy rainfall area from the analysis of these data. It knows that the land-slides are triggered by the heavy rainfall. Meanwhile, the rainfall factors such as the accumulative rainfall, the rain-fall intensity and the dropping time are different in each landslides area, and the shape and frequency of landslides are different respectively. The landslides have occurred in the area of high accumulative rainfall, while the land-slides have not occurred around that area. Therefore, the rainfall is very important factor induced by the landslides, and the accumulative rainfall is really related to the frequency of landslides.

• Korean Journal of Remote Sensing
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• v.12 no.2
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• pp.126-138
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• 1996

The characteristics of rapid development of the low pressure system over the East Sea from 06 to 08 Nov., 1995 has been analyzed in detail by the synoptic numerical products and satellite observations. The Low system was initially triggered the development of the baroclinic leaf cloud over the border of the northern part of Korea and China and moved eastward and then developed explosively com-ma or lambda type cloud system over the East Sea. To forecast well the general development and movement of the coastal winter cyclone over the East Sea popularly in a numerical simulation by several scientists, the large baroclinicity, continuous support of water vapor, and sequential cold outbreak over the warm sea surface have been more commonly concerned about. The cyclone which the central surface pressure was dropped 40hPa within 24 hours has often accompanied strong wind and heavy snow- or rain-fall in the winter season. In all successive observations with 12-hourly satellite imagery and analyzed meteorological variables in this period, the centers of the sea-level pressure and 500hPa geopotential height associated with this cyclone were typically illustrated by moving farther eastward using GMS combined enhanced IR images. The maxi-mum wind sustained by this system with the intensity and central pressure of tropical storm was about 60 knots with the center pressure drop of 44hPa/day similar to the North American cyclonic bomb and Atlantic storm.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.6
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• pp.667-676
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• 2006

The reduction of $Hg^{2+}$ in the aqueous phase results in the production of dissolved gaseous mercury(DGM), and the volatilization of DGM has been identified as an important mechanism for the loss of Hg from waterbodies to the atmosphere. Although mercury emission in the world is known to be mostly from Asia, there have been few studies of measuring DGM concentrations in lakes in Asia. In this study, DGM concentrations were measured at Juam reservoir($35^{\cir}00'N,\;127^{\circ}14'E$), Korea. The results showed that the average concentrations of DGM at the upper and down stream of the lake during summer time were $95{\pm}8\;and\;130{\pm}15$ pg/L, respectively and the concentration of total mercury(TM) at the upper and down stream was $2.1{\pm}0.7,\;1.7{\pm}0.3$ ng/L respectively. Average DGM concentration during summer time($101{\pm}14pg/L$) was approximately 5.5 times higher than that during fall($18{\pm}0.1pg/L$). The DGM concentrations ai the midstream decreased from 32 to 13.7 pg/L during rain event, while the TM concentrations increased from 2.2 ng/L to 2.7 ng/L indicating the deposition of mercury from the atmosphere. Also, the diurnal patterns between DGM concentrations and UV intensities were observed. Water temperatures and DOC concentrations were significantly related to DGM concentrations, while TM concentrations were negatively related to DGM concentrations(p<0.0001). Comparing with the study of Dill et al.,(2006) the average concentrations of DGM $(109{\pm}15pg/L)\;and\;TM(2.2{\pm} 0.4ng/L)$ at Juam reservoir were approximately 3 and 2.2 times higher than those measured in other lakes(DGM: $38{\pm}16pg/L$, TM: $1.0{\pm}1.2ng/L$).

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.981-992
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• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.