• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.155-164
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• 2005

The coast has been known as very vulnerable area. This area has nature disasters such as typhoon, tidal wave, flood and storm almost every year. In this study, coast vulnerable area information management system was developed to manage the coastal facilities and vulnerable area through Web GIS. This system is able to visualize the damage area and support the official work related to coast as efficient DSS(Decision Supporting System). Moreover, the foundation for domestic coast information management is expected by acquiring less cost and time. For this, GIS DB was first constructed by acquiring damage factor data such as typhoon, tidal wave, flood and storm. Then GIS analysis methods and high resolution satellite images are used to possibly present the results of retrieve as table, map, graph, inundation simulation in real time.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.671-683
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• 2018

The flood damage caused by heavy rains in urban watershed is increasing, and, as evidenced by many previous studies, urban flooding usually exceeds the water capacity of drainage networks. The flood on the area which considerably urbanized and densely populated cause serious social and economic damage. To solve this problem, deterministic and probabilistic studies have been conducted for the prediction flooding in urban areas. However, it is insufficient to obtain lead times and to derive the prediction results for the flood volume in a short period of time. In this study, IDNN, TDNN and NARX were compared for real-time flood prediction based on urban runoff analysis to present the optimal real-time urban flood prediction technique. As a result of the flood prediction with rainfall event of 2010 and 2011 in Gangnam area, the Nash efficiency coefficient of the input delay artificial neural network, the time delay neural network and nonlinear autoregressive network with exogenous inputs are 0.86, 0.92, 0.99 and 0.53, 0.41, 0.98 respectively. Comparing with the result of the error analysis on the predicted result, it is revealed that the use of nonlinear autoregressive network with exogenous inputs must be appropriate for the establishment of urban flood response system in the future.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.929-941
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• 2008

The objectives of this study are to propose a system for combined use of a hydrologic and a hydraulic model for urban flood forecast model and to evaluate the system on the $300km^2$ Jungrang urban watershed area, which is relatively large area as an urban watershed and consequently composed of very complex drainage pipes and streams with different land uses. In this study, SWMM for hydrologic model and HEC-RAS for hydraulic model are used and the study area is divided into 25 subbasins. The SWMM model is used for sewer drainage analysis within each subbasin, while HEC-RAS for unstready flow analysis in the channel streams. Also, this study develops a GUI system composed of mean areal precipitation input component, hydrologic runoff analysis component, stream channel routing component, and graphical representation of model output. The proposed system was calibrated for the model parameters and verified for the model applicability by using the observation data. The correlation coefficients between simulated and observed flows at the 2 important locations were ranged on 0.83-0.98, while the coefficients of model efficiency on 0.60-0.92 for the verification periods. This study also provided the possibilities of manhole overflows and channel bank inundation through the calculated water profile of longitudinal and channel sections, respectively. It can be concluded that the proposed system can be used as a surface runoff and channel routing models for urban flood forecast over the large watershed area.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.398-398
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• 2015

To estimate and forecast runoff by using Aritifitial Neaural Networks model (ANNs). it has been studied in Thailand for the past 10 years. The model was developed in order to be conformed with the conditions in which the collected dataset is short and the amount of dataset is inadequate. Every year, the Northerpart of Thailand faces river overflow and flood inundation. The most important basin in this area is Yom basin. The purpose of this study is to forecast runoff at Y.14 gauge station (Si-Satchanalai district, Sukhothai province) for 3 days in advance. This station located at the upstream area of Yom River basin. Daily rainfall and daily runoff from Royal Irrigation Department and Meteorological Department during flood period 2000-2012 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination ($R^2$). The result of the first day gets the highest accuracy and then decreased in day 2 and day 3, consequently. NSE and $R^2$ values for frist day of runoff forecasting is 0.76 and 0.776, respectively. On the second day, those values are 0.61 and 0.65, respectively. For the third day, the aforementioned valves are 0.51 and 0.52, respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and insufficient. In conclusion, the ANNs model is suitable for applying during flood incident because it is easy to use and does not require numerous parameters for simulating.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.10-18
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• 2022

Purpose: For smooth performance of flood analysis due to heavy rain disasters at energy storage facilities in the Incheon area, field surveys, observational surveys, and pre-established reports and drawings were analyzed. Through the field survey, the characteristics of pipelines and rivers that have not been identified so far were investigated, and based on this, the input data of the SWMM model selected for inundation analysis was constructed. Method: In order to determine the critical duration through the probability flood analysis according to the calculation of the probability rainfall intensity by recurrence period and duration, it is necessary to calculate the probability rainfall intensity for an arbitrary duration by frequency, so the research results of the Ministry of Land, Transport and Maritime Affairs were utilized. Result: Based on this, the probability of rainfall by frequency and duration was extracted, the critical duration was determined through flood analysis, and the rainfall amount suggested in the disaster prevention performance target was applied to enable site safety review. Conclusion: The critical duration of the base was found to be a relatively short duration of 30 minutes due to the very gentle slope of the watershed. In general, if the critical duration is less than 30 minutes, even if flooding occurs, the scale of inundation is not large.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.2
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• pp.459-466
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• 2017

Several reservoirs that were managed by local governments and the Korea Rural Community Corporation have recently collapsed. One of them is the Goeyeon reservoir in Yeongcheon-si, Gyeongsangbuk-do that collapsed mainly around the spillway due to heavy rain at 9 O'clock, on 21 August 2014. The Goeyeon reservoir was an aging agricultural reservoir over 70 years since it was built. In this study, the collapse situation of the reservoir was reproduced through the DAMBRK model. Flood inundation maps were reconstructed for the breach outflow of the dam analyzed by the DAMBRK model. We estimated the breach duration and outflow of the reservoir as compared with the inundation image taken by the Unmanned Aerial Vehicle (UAV) at the time when the Goeyeon reservoir collapsed. The results of this study are expected to be useful for predicting damage in the downstream inundation area when a reservoir collapses.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.4
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• pp.337-342
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• 2001

A natural disaster occupies a considerable part among various damages, and the damage of human lifes and property by heavy rain extends to hundreds, and billions in every you. In old times, flood was mainly occurred in big river or sudden slope, but these days, the damage of concentrated heavy rain is being extended to a city. Recently, very big floods occurred continuously, so real time submersion expectation system which can expect the inundation boundary according to the scale is needed so as to protect lifes and property. In this study, in and around Jungrang river, where the damage of flood is big, is chosen as a sample, and the submersion of that area is expected by analyzing the flux and overflowing using DEM, and connecting with Web GIS in real time.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.447-447
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• 2015

최근 기후변화에 따른 전 지구적인 지구온난화는 단시간의 집중호우와 돌발홍수의 증가로 기존의 기후특성을 변화시키고 있다. 이로 인해, 자연재해의 강도가 강해지고, 재산피해가 커지고 있다. 특히, 내륙에 위치한 도시지역 보다 해안 도시지역은 조위에 따라 홍수위가 크게 영향을 받기 때문에 강우에 따른 피해규모는 더 크게 영향을 받는다. 본 연구에서는 기후변화로 인한 자연재해에 대비하기 위하여 미래 기후변화를 예측하고 해안 도시지역에 미치는 영향을 파악하고자 하였으며, 대상지역으로는 2012년 태풍 산바(Sanba)으로 인해 상당한 인명피해와 홍수피해가 발생한 마산(창원시) 일대를 선정하였다. 본 연구에서는 마산(창원시) 대상으로 빈도-지속기간별 강우와 조위의 영향을 고려한 침수모의를 실시하고자 한다. 또한 2004년도에 개발된 다차원법(다차원 홍수피해 산정방법(Multi-Dimensional Flood Damage Analysis))을 이용하여 조위와 홍수위의 영향을 받은 해안 도시의 경제성 분석을 실시하고, 침수에 따른 피해액을 산정하고자 한다. 본 연구의 결과는 향후 마산(창원시) 일대의 홍수피해 산정과 침수피해 관련 방재 정책을 수립하는데 기초자료로 활용할 수 있을 것으로 기대된다.

• 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.