• Proceedings of the Korea Water Resources Association Conference
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• 1999.05a
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• pp.141-146
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• 1999

• Water for future
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• v.54 no.7
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• pp.53-60
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• 2021

• Journal of Ocean Engineering and Technology
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• v.23 no.4
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• pp.25-31
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• 2009

To predict pollutants during the flood and dry season in Gwangyang Bay, the net-fluxes and pollutant material budgets of COD, T-N, and T-P were calculated in Gwangyang Bay using a 2-D hydrodynamic model. Calculating the net-flux for each area in Gwangyang Bay showed that the net-fluxes in regions IV, V, and VII were increasing, but those of regions II, III, and VI were decreasing. In budget calculations for COD, T-N, and T-P in Gwangyang Bay, it was estimated that during the dry season the COD is approximately 1.6 times higher than during the flood season. The T-N during the flood season is approximately 7 times higher than during the dry season. However, the material budget for T-P in Gwangyang Bay predicted that it is almost nonexistent. Moreover, the central part of Gwangyang Bay (Region IV) has the highest material budget of overall pollutants.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.2
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• pp.57-65
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• 1986

The need of forecasting river flows arised whenever a river authority must make controls to protect the life and property from the flood and maintain the adequate flows for water use. This study is on the real time flood forecasting from the gauged and ungauged rainfall input and identification of second-order autoregressive(AR(2)) which is used as system model. A Kalman filter is used to obtain the values of the system parameters needed for the optimal control strategy. This system model was applied to the data at the Naiu gauging station in Young san river basin to check the accuracy and efficiency of prediction. One step ahead prediction is checked by stochastic analysis and the order of autoregressive model is proved to be satisfied, Discussions on interesting features of the model are presented.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.181-184
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• 2007

Up to now, a lot of houses, roads and other urban facilities have been damaged by natural disasters such as flash floods and landslides. It is reported that the size and frequency of disasters are growing greatly due to global warming. In order to mitigate such disaster, flood forecasting and alerting systems have been developed for the Han river, Geum river, Nak-dong river and Young-san river. These systems, however, do not help small municipal departments cope with the threat of flood. In this study, a real-time urban flood forecasting service (U-FFS) is developed for ubiquitous computing city which includes small river basins. A test bed is deployed at Tan-cheon in Gyeonggido to verify U-FFS. Wireless sensors such as rainfall gauge and water lever gauge are installed to develop hydrologic forecasting model and CCTV camera systems are also incorporated to capture high definition images of river basins. U-FFS is based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) that is data-driven model and is characterized by its accuracy and adaptability. It is found that U-FFS can forecast the water level of outlet of river basin and provide real-time data through internet during heavy rain. It is revealed that U-FFS can predict the water level of 30 minutes and 1 hour later very accurately. Unlike other hydrologic forecasting model, this newly developed U-FFS has advantages such as its applicability and feasibility. Furthermore, it is expected that U-FFS presented in this study can be applied to ubiquitous computing city (U-City) and/or other cities which have suffered from flood damage for a long time.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.515-526
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• 2021

The frequency of typhoons and torrential rainfalls has increased due to climate change, and the concurrent risk of breakage of dams and reservoirs has increased due to structural aging. To cope with the risk of dam breakage, a more accurate emergency action plan (EAP) must be established, and more advanced technology must be developed for the prediction of flooding. Hence, the present study proposes a method for establishing a more effective EAP by performing flood and inundation analyses using one- and two-dimensional models. The probable maximum flood (PMF) under the condition of probable maximum precipitation (PMP) was calculated for the target area, namely the Gyeong-cheon reservoir watershed. The breakage scenario of the Gyeong-cheon reservoir was then built up, and breakage simulations were conducted using the dam-break flood forecasting (DAMBRK) model. The results of the outflow analysis at the main locations were used as the basis for the one-dimensional (1D) and two-dimensional (2D) flood inundation analyses using the watershed modeling system (WMS) and the FLUvial Modeling ENgine (FLUMEN), respectively. The maximum inundation area between the Daehari-cheon confluence and the Naeseong-cheon location was compared for each model. The 1D flood inundation analysis gave an area of 21.3 km², and the 2D flood inundation analysis gave an area of 21.9 km². Although these results indicate an insignificant difference of 0.6 km² in the inundation area between the two models, it should be noted that one of the main locations (namely, the Yonggung-myeon Administrative and Welfare Center) was not inundated in the 1D (WMS) model but inundated in the 2D (FLUMEN) model.

• 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.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1285-1294
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• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.203-222
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• 2011

The characteristics of flood damages have been increasingly strengthened and take the form of unpredictable and unusual weather phenomena caused by climate change and climate anomalies. To prevent inundation damage caused by breach of hydraulic structure such as dam or levee, and trouble of drainage of inner basin, the prediction necessity of flood inundation area, flood risk analysis, and drawing flood risk maps have been on the rise, and the national flood risk maps have been produced. In this study, the quantitative flood vulnerability analysis was performed, which represents population living within flood-affected areas, types of economic activities, facilities affected by flood, in order to extend flood risk mapping from simple hazard concept into risk based idea. By applying it to Nakdong River basin, the flood vulnerability indices were estimated to draw flood risk maps subdivided into administrative districts. The result of this study can be applied to establish the disaster prevention measures and priority decision of disaster prevention project.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.294-294
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• 2019

The system for predicting flood of river at Flood Control Office is made up of a rainfall-runoff model and FLDWAV model. This system is mainly operating to predict the excess of the flood watch or warning level at flood forecast points. As the demand for information of the management and operation of riverside, which is being used as a waterfront area such as parks, camping sites, and bike paths, high-level forecasts of watch and warning at certain points are required as well as production of lowland flood forecast information that is used as a waterfront within the river. In this study, a technology to produce flood forecast information in lowland areas of the river used as a waterfront was developed. Based on the results of the 1D hydraulic analysis, a model for performing spatial operations based on high resolution grid was constructed. A model was constructed for Andong district, and the inundation conditions and level were analyzed through a virtual outflow scenarios of Andong and Imha Dam.