• Korean Journal of Remote Sensing
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• v.2 no.2
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• pp.117-131
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• 1986

The purpose of this study was to develop techniques acquiring hydrologic parameters that affect runoff conditions from Landsat imagery. Runoff conditions in a study area were analyzed by employing the U.S. Soil Conservation Service(SCS) Method. SCS runoff curve numbers(CN) were estimated by the computer analysis of Landsat imagery and digiral terrain model(DTM) data. The SCS Method requires land use/cover and soil conditions of the area as input parameters. A land use/cover map of 5 hydrological classes was produced from the Landsat multi-spectral scannerr imagery. Slope-gradient and contour and contour maps were also made using the DTM topographic data. Inundation areas depending on reservoir levels were able to be mapped on the Landsat scene by combining the contour data.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.485-489
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• 2008

To achieve the optimal sewer layout design, most developed models are designed to determine pipe diameter, slope and overall layout in order to minimize the least cost for the design rainfall. However, these models are not capable of considering the superposition effect of runoff hydrographs entering each junction. The suggested Optimal Sewer Layout Model (OSLM) is designed to control flows and distribute the node inflows while taking into consideration the superposition effect for reducing the inundation risk from the sewer pipes. The suggested model used the genetic algorithm to determine the optimal layout, which was connected to the SWMM (Storm Water Management Model) for the calculation of the hydraulic analysis. The suggested model was applied to an urban watershed of 35 ha, which is located in Seoul, Korea. By using the suggested model, several rainfall events, including the design rainfall and excessive rainfalls, were used to generate runoff hydrographs from a modified sewer layout. By the results, the peak outflows at the outlet were decreased and the overflows were also reduced.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.5
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• pp.492-501
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• 2007

Coastal disasters have become one of the most important issues in every coastal country. In Korea, coastal disasters such as storm surge, sea level rise and extreme weather have placed many coastal regions in danger of being exposed or damaged during subsequent storms and gradual shoreline retreat. A storm surge is an onshore gush of water associated with a tow pressure weather system, typically in typhoon season. However, it is very difficult to predict storm surge height and inundation due to the irregularity of the course and intensity of a typhoon. To provide a new scheme of typhoon damage prediction model, the scenario which changes the central pressure, the maximum wind radius, the track and the proceeding speed by corresponding previous typhoon database, was composed. The virtual typhoon scenario database was constructed with individual scenario simulation and evaluation, in which it extracted the result from the scenario database of information of the hereafter typhoon and information due to climate change. This virtual typhoon scenario database will apply damage prediction information about a typhoon. This study performed construction and analysis of the simulation system with the storm surge/coastal inundation model at Masan coastal areas, and applied method for predicting using the scenario of the storm surge.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.126-126
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• 2021

Stormwater reduction plays an important role in the safety and resilience to flooding in urban areas. Due to rapid climate change, the world is experiencing abnormal climate phenomena, and sudden floods and concentrated torrential rains are frequently occurring in urban basins and the amount of outflow due to stormwater increases. In addition, the damage caused by urban flooding and inundation due to extreme rainfall exceeding the events that occurred in the past increases. To solve this problem, water supply, drainage, and water supply for sustainable urban development, the water management paradigm is shifting from sewage maintenance to water circulation and water-sensitive cities. So, in this study, The purpose of this study is to examine measures to increase the resilience of urban ecosystem systems for urban excellence reduction by analyzing the effects of green infra structures and LID techniques and evaluating changes in resilience. In this study, for simulating and analysis of runoff for various stormwater patterns and LID applications, Storm Water Management Model (SWMM) was used.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.2
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• pp.49-56
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• 2017

In this study, flood analysis was conducted to prepare for damage caused by typhoons and heavy rain due to abnormal climate and climate change. Two - dimensional flooding analysis using the FLUMEN model, which is widely used for national and international flood risk mapping, was conducted for the Nam River Basin, which is the tributary of the Nakdong River. This study divides the topography into $5m{\times}5m$ DEM by ArcView, so that the accuracy of river repair and hydrological characterization and flood area identification can be maximized. As a result of simulation of water flooding, 163.3ha in section 1, 227.7ha in section 2 and 59.9ha in section 3 were simulated.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.92-104
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• 2015

In order to analyze the impacts of climate change, a time and space integrated model was developed in this study using system dynamics and GIS. The model built was used to carry out a simulation on the inundation impact on A-gu of Busan Metropolitan city resulting from the sea level rise scenario of IPCC and storm surge, which is the worst case. Through this, the flooded area and population until 2100 were predicted. Also, the result and significance of each alternative was reviewed improving the model by establishing alternative scenarios of protection, accommodation and retreat as plans of reaction to sea level rise. The combination of system dynamics and GIS has advantages of how the diverse variables change until the target year can be traced and, accordingly, not only the results but also the processes of spatial change can be examined by calculating the value of change process at each time step. The synergy of this model presumed to be a foothold for solving problems which are becoming difficult to predict due to increase in uncertainty and complexity such as the support for decision making for urban resilience to natural disasters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Water for future
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• v.28 no.5
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• pp.163-174
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• 1995

A diffusion hydrodynamic model named "DFLOW-2" for the floodwave analysis from levee-break in protected lowland has been developed. The model has been applied to Ilsan levee-break, which occurred on September 12-13, 1990 in the downstream of the Han River. An unsteady flow analysis has been made in the reach from Indokyo to Junryu. Overflow through broken levee has been treated as internal boundary condition in the channel. A post-processor has been also developed to demonstrate the simulation results. The velocity distributions and inundated depths have been presented. The computed results have good agreements with observed data in terms of inundation depth, flood arrival time and flooded areas.ded areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.123-123
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• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• Journal of Korea Water Resources Association
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• v.52 no.5
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• pp.361-372
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• 2019

The purpose of this study is to develop a two-dimensional land surface flood analysis model based on uniform square grid using the governing equations except for the convective acceleration term in the momentum equation. Finite volume method and implicit method were applied to spatial and temporal discretization. In order to reduce the execution time of the model, parallel computation techniques using CPU were applied. To verify the developed model, the model was compared with the analytical solution and the behavior of the model was evaluated through numerical experiments in the virtual domain. In addition, inundation analyzes were performed at different spatial resolutions for the domestic Janghowon area and the Sebou river area in Morocco, and the results were compared with the analysis results using the CAESER-LISFLOOD (CLF) model. In model verification, simulation results were well matched with the analytical solution, and the flow analyses in the virtual domain were also evaluated to be reasonable. The results of inundation simulations in the Janghowon and the Sebou river area by this study and CLF model were similar with each other and for Janghowon area, the simulation result was also similar to the flooding area of flood hazard map. The different parts in the simulation results of this study and the CLF model were compared and evaluated for each case. The results of this study suggest that the model proposed in this study can simulate the flooding well in the floodplain. However, in case of flood analysis using the model presented in this study, the characteristics and limitations of the model by domain composition method, governing equation and numerical method should be fully considered.