• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1195-1204
• /
• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• Journal of Korean Society of Disaster and Security
• /
• v.12 no.2
• /
• pp.73-82
• /
• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.281-281
• /
• 2015

Shallow landslide often occurs in areas of this topography where subsurface soil water flow paths give rise to excess pore-water pressures downslope. Recent hillslope hydrology studies have shown that subsurface topography has a strong impact in controlling the connectivity of saturated areas at the soil-bedrock interface. In this study, the physically based SHALSTAB model was used to evaluate the effects of three soil thicknesses (i.e. average soil layer, soil thickness to weathered soil and soil thickness to bedrock soil layer) and subsurface flow reflecting three soil thicknesses on shallow landslide prediction accuracy. Three digital elevation models (DEMs; i.e. ground surface, weathered surface and bedrock surface) and three soil thicknesses (average soil thickness, soil thickness to weathered rock and soil thickness to bedrock) at a small hillslope site in Jinbu, Kangwon Prefecture, eastern part of the Korean Peninsula, were considered. Each prediction result simulated with the SHALSTAB model was evaluated by receiver operating characteristic (ROC) analysis for modelling accuracy. The results of the ROC analysis for shallow landslide prediction using the ground surface DEM (GSTO), the weathered surface DEM and the bedrock surface DEM (BSTO) indicated that the prediction accuracy was higher using flow accumulation by the BSTO and weathered soil thickness compared to results. These results imply that 1) the effect of subsurface flow by BSTO on shallow landslide prediction especially could be larger than the effects of topography by GSTO, and 2) the effect of weathered soil thickness could be larger than the effects of average soil thickness and bedrock soil thickness on shallow landslide prediction. Therefore, we suggest that using BSTO dem and weathered soil layer can improve the accuracy of shallow landslide prediction, which should contribute to more accurately predicting shallow landslides.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.142-142
• /
• 2021

An arbitrary representation of an urban drainage sewer system was devised using a geographic information system (GIS) tool in order to calculate the surface and subsurface flow interaction for simulating urban flood. The proposed methodology is a mean to supplement the unavailability of systematized drainage system using high-resolution digital elevation(DEM) data in under-developed countries. A modified DEM was also developed to represent the flood propagation through buildings and road system from digital surface models (DSM) and barely visible streams in digital terrain models (DTM). The manhole, sewer pipe and storm drain parameters are obtained through field validation and followed the guidelines from the Plumbing law of the Philippines. The flow discharge from surface to the devised sewer pipes through the storm drains are calculated. The resulting flood simulation using the modified DEM was validated using the observed flood inundation during a rainfall event. The proposed methodology for constructing a hypothetical drainage system allows parameter adjustments such as size, elevation, location, slope, etc. which permits the flood depth prediction for variable factors the Plumbing law. The research can therefore be employed to simulate urban flood forecasts that can be utilized from traffic advisories to early warning procedures during extreme rainfall events.

• Journal of The Geomorphological Association of Korea
• /
• v.23 no.3
• /
• pp.67-78
• /
• 2016

Soil carbon(C) is an essential property for characterizing soil quality. Understanding spatial patterns of soil C is particularly limited for mountain areas. This study aims to predict the spatial pattern of soil C using terrain analysis in a steep mountainous area. Specifically, model performances and prediction uncertainties were investigated based on the number of resampling repetitions. Further, important predictors for soil C were also identified. Finally, the spatial distribution of uncertainty was analyzed. A total of 91 soil samples were collected via conditioned latin hypercube sampling and a digital soil C map was developed using support vector regression which is one of the powerful machine learning methods. Results showed that there were no distinct differences of model performances depending on the number of repetitions except for 10-fold cross validation. For soil C, elevation and surface curvature were selected as important predictors by recursive feature elimination. Soil C showed higher values in higher elevation and concave slopes. The spatial pattern of soil C might possibly reflect lateral movement of water and materials along the surface configuration of the study area. The higher values of uncertainty in higher elevation and concave slopes might be related to geomorphological characteristics of the research area and the sampling design. This study is believed to provide a better understanding of the relationship between geomorphology and soil C in the mountainous ecosystem.

• Tunnel and Underground Space
• /
• v.17 no.5
• /
• pp.360-371
• /
• 2007

This paper presents an application of drainage control and slope stability by GIS-based hydrological modeling to control the surface water from an operational point of view. This study was carried out on a region of Pasir open-pit coal mine, Indonesia. A detailed topographical survey was performed at the study area to generate a reliable DEM (Digital Elevation Model). Hydrology tools implemented in ArcGIS 9.1 were used to extract the characteristics of drainage system such as flow direction, flow accumulation and catchment area from DEM. The results of hydrological modeling and spatial analysis showed that current arrangement of pumping facility is not suitable and some vulnerable places to erosion exist on the bench face due to concentrated surface runoff. Finally, some practical measures were suggested to optimize the design of drainage system and to monitor the slope stability by the surface water management at the study region during heavy rainfall.

• Journal of the Korean Geographical Society
• /
• v.40 no.3 s.108
• /
• pp.296-309
• /
• 2005

In this study, we investigated the change of future land-surface and relationships of land-surface change with geo-spatial information, using a Bayesian prediction model based on a likelihood ratio function, for analysing the land-surface change of the Gongju area. We classified the land-surface satellite images, and then extracted the changing area using a way of post classification comparison. land-surface information related to the land-surface change is constructed in a GIS environment, and the map of land-surface change prediction is made using the likelihood ratio function. As the results of this study, the thematic maps which definitely influence land-surface change of rural or urban areas are elevation, water system, population density, roads, population moving, the number of establishments, land price, etc. Also, thematic maps which definitely influence the land-surface change of forests areas are elevation, slope, population density, population moving, land price, etc. As a result of land-surface change analysis, center proliferation of old and new downtown is composed near Gum-river, and the downtown area will spread around the local roads and interchange areas in the urban area. In case of agricultural areas, a small tributary of Gum-river or an area of local roads which are attached with adjacent areas showed the high probability of change. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the capability of forest damage is very high. As a result of validation using a prediction rate curve, a capability of prediction of urban area is $80\%$, agriculture area is $55\%$, forest area is $40\%$ in higher $10\%$ of possibility which the land-surface change would occur. This integration model is unsatisfactory to Predict the forest area in the study area and thus as a future work, it is necessary to apply new thematic maps or prediction models In conclusion, we can expect that this way can be one of the most essential land-surface change studies in a few years.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.10a
• /
• pp.1712-1716
• /
• 2008

Performance of ground improvement project using prefabricated vertical drains of condition, in which approximately 10m dredged fill overlies original soft foundation layer in the coastal area has been conducted. From field monitoring results, excessive ground settlement compared to predicted settlement in design stage developed during the following one year. In order to predict the final consolidation behavior, recalculation of consolidation settlements and back analysis using observed settlements were conducted. Field monitoring results of surface settlements were evaluated, and then corrected because large shear deformation was occurred by construction events in the early stages of consolidation. To predict the consolidation behavior, material functions and in-situ conditions from laboratory consolidation test were re-analyzed. Using these results, height of additional embankment is estimated to satisfy residual settlement limit and maintain an adequate ground elevation. The recalculated time-settlement curve has been compared to field monitoring results after additional surcharge was applied.

• Journal of Environmental Science International
• /
• v.30 no.12
• /
• pp.1067-1079
• /
• 2021

The coastal water is a space where salmon (Oncorhynchus keta), critical energy-conveying mediator, stay to adapt to different environments while traveling between ocean and river ecosystems for spawning and growth. The mid-eastern coast of Korea (MECW) is the southern limit of salmon distributed in the North Pacific Ocean. Understanding the distribution and migration characteristics of salmon in the MECW is important for the prediction of changes in the amount and distribution of salmon related to changes in the future marine environment. We analyzed the relationship between the salmon migratory timing ascending the Wangpi river and change in vertical seawater temperature and tidal elevation. Overall results highlight that (1) Salmon began to ascend the river when the sea surface water temperature (SST) decreased below 20℃; (2) The number of salmon ascending the river increased when the temperature difference between the upper and lower layers decreased, but decreased when the temperature difference was higher than 5℃; (3) The number of salmon ascending the river peaked, when the SST was 18℃-19℃ and sea levels rose at high tide. This study provide important insight into predicting changes in the ecosystem energy circulation through climate change at its southern distribution limit.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.18 no.1
• /
• pp.95-107
• /
• 2016

Ground water is one of important parameters in the designs of underpass structures because urban areas are characterized by soil ground which is relatively permeable than rock ground and a high level of ground water due to low elevation. Therefore, it is important properly to predict variations of the ground water when they can affect underpass structures. In this study, a series of numerical analyses are performed to predict the variations of ground water levels considering the degree of surface imperviousness and LID(Low Impact Development) application. In turn the stability of underground structure is assessed using predicted ground water level. The results show that an increase in the impervious surface area decreases the ground water level. The application of permeable pavement as a LID facility increases the ground water level, improving the infiltration capacity of rainfall into the ground. Seasonal variations of the ground water level are also verified in numerical simulation. The results of this study suggest that reasonable designs of underpass structures can be obtained with the suitable prediction and application of the ground water level considering the surface characteristics.