• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.187-187
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• 2016

High intensity of population distribution in deltaic setting especially in Asia tends to have increased and causes coastal flood risk due to lower elevations and significant subsidence. Maximum or peak discharge of flood always causes numerous deaths and huge economic losses. New technology of spatial satellite image has been applied to analyze flood damage. In this research, the relationship of nightlight intensity associated with flood damages has been determined during 1992-2013 with spatial resolution of 30 arc sec ($0.0083^{\circ}$) which is nearly one kilometer at the equator in whole six countries along the Mekong River (i.e., China, Myanmar, Lao PDR, Thailand, Cambodia and Vietnam). ArcGIS Hydrological Flow Length Tool has been used to determine the distance of each pixel areas from the rivers and streams. Statistical analysis results highlight the significant correlation R = 0.47 between nightlight digital number and economic damages per unit area (US$/km2) and R = 0.62 for number of affected people for unit area ($people/km^2$). The areas near by the Mekong River and its tributaries correspond to high flood damage. This spatial analysis result is going to be prestigious key information to the regions and all related stakeholders for decisions and mitigation strategies.

• Current Research on Agriculture and Life Sciences
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• v.7
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• pp.55-66
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• 1989

The purpose of this study is to estimate the flood discharge and peak time by the SCS method and the probability method using the geomorpologic parameters obtained from the topographic maps following the law of stream classifying and, ordering by Horton and Strahler. The SCS method and the probability method are used in estimating the times to peak and the flood discharges at An-dong, Im-ha, and Sun-san basins in the Nakdong River system. The results obtained are as follows : 1. The range of the values of the area ratio, the bifurcation ratio and the length ratio agree with those of natural streams presented by Horton and Strahler. 2. Comparisons of the probability method and observed values show that small relative errors of 0-7% of flood discharge, and 0-2hr, difference in time to peak respectivly. But the SCS method shows that large relative errors of 10-40% of flood discharge, and 0-4hr, difference in time to peak. 3. When the rainfall intensity is large, the error of flood discharge estimated by using the probability method is relativly small.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.1
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• pp.51-64
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• 2020

The purpose of this study is to present a method for quantitative analysis of flooding at the flood plain in an ungauged area using satellite rainfall and global geographic data. For this, flooding of the Tumen/Namyang area in the Duman-gang(Riv.) was simulated and the flood conditions were quantitatively analyzed. The IMERG data, a rainfall data derived from satellite images, was used as rainfall data. The GRM model was applied to the watershed runoff simulation, and the G2D model was applied to the flooding simulation of the Tumen/Namyang area. Flood event caused by Typhoon Lionrock in August 2016 was applied. Recorded peak discharge of the Tumen/Namyang region was used to verify the runoff simulation results. To verify the result of the inundation simulation, the flood situation collected through field survey and satellite image data before and after the flood were used. The peak flow rates by the runoff simulation and flood record were 7,639㎥/s and 7,630㎥/s, respectively, with a relative error of about 0.1%. In the flood simulation, the results were similar to the flooding ranges identified in the survey data and satellite images. And the changes of flooding depth and flooding time in the flood plain in Tumen/Namyang area could also be assessed. The methods and results of this study will be useful for the quantitative assessment of floods in the ungauged areas.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.1 s.12
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• pp.41-50
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• 2004

The objective of this study is to investigate the feasibility of the developed Fuzzy control techniques in dam operation. The simulated results for the 1984, 1990, and 1995 flood events are compared with historical operation results in the view of flood control and disaster prevention. The three models developed in this study determine the outflows based on the two conditions the first one is to consider only two inputs such as reservoir water level and inflow, for operation of the existing situations, the second one is that the possible maximum discharge from each dam does not exceed the allowable design maximum discharge for disaster prevention in downstream area. As the results, it was shown that the suggested models based on Fuzzy control technique could reduce both the peak water level and the maximum peak discharge compared with the historical operation results.

• Water for future
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• v.28 no.3
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• pp.187-195
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• 1995

This study aims at suggesting an alternative to improve current capacity of flood control for the existing Soyanggang multi-purpose dam which was constructed 20 years ago as a largest dam in Korea. The newly estimated value of the probable maximum precipitation(PMP) is 760.0 mm which is based on the hydrometeorological method. The peak inflow of 1000 years return period at the time of construction was 13,500$m^3$/s. However, the newly estimated peak inflow of the PMF is 18,100$m^3$/s which is 1.34 times bigger than the original one. In order to adopt the newly estimated PMF as a design flood, following four alternatives were compared; (1) allocation of more flood control space by lowering the normal high water level, (2) construction of a new spillway in addition to the existing one, (3) raising the existing dam crest, (4) construction of a new dam which has relevant flood control storage at the upstream of the Soyanggang multipurpose dam. The preliminary evaluation of these alternatives resulted in that the second alternative is most economical and feasible. So as to stably cope with the newly estimated PMF by meeting all the current functions of the multi-purpose dam, a detailed study of an additional spillway tunnel has to be followed.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.11-25
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• 2021

The objective of this study was to analyze the impact of activation functions on flood forecasting model based on Artificial neural networks (ANNs). The traditional activation functions, the sigmoid and tanh functions, were compared with the functions which have been recently recommended for deep neural networks; the ReLU, leaky ReLU, and ELU functions. The flood forecasting model based on ANNs was designed to predict real-time runoff for 1 to 6-h lead time using the rainfall and runoff data of the past nine hours. The statistical measures such as R2, Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), the error of peak time (ETp), and the error of peak discharge (EQp) were used to evaluate the model accuracy. The tanh and ELU functions were most accurate with R2=0.97 and RMSE=30.1 (㎥/s) for 1-h lead time and R2=0.56 and RMSE=124.6~124.8 (㎥/s) for 6-h lead time. We also evaluated the learning speed by using the number of epochs that minimizes errors. The sigmoid function had the slowest learning speed due to the 'vanishing gradient problem' and the limited direction of weight update. The learning speed of the ELU function was 1.2 times faster than the tanh function. As a result, the ELU function most effectively improved the accuracy and speed of the ANNs model, so it was determined to be the best activation function for ANNs-based flood forecasting.

• Journal of Industrial Technology
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• v.3
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• pp.95-101
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• 1983

This study aims at the determination of the coefficienties of runoff and infiltration affecting runoff. The rating curve is more available than the peak flood runoff to determine flood control plan of flood control reservoir and the volume of hydroelectric power plant, or to make multipurpose dam. In hydrologic analysis and design, it is necessary to develop relations between precipitation and runoff, possible using some of the factors affecting runoff as parameters. In order to calculate the runoff discharge, the runoff process constituting elements are divided to the surface runoff, the subsurface runoff and the groundwater runoff. By comparing the computed hydrograph with the measured hydrograph, determinned the watershed TANK Model constant Varying the tank model constant for approximating the computed hydrograph to the measured hydrograph.

• Journal of Korean Society on Water Environment
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• v.25 no.6
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• pp.935-942
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• 2009

During period of the rainy season of spring tide Aug. 2005, the suspended sediment transport rate from Seomjin River increased ten times as high as neap tide of low river discharge. During ebb tide of high terrestrial input, the grain size of suspended particles of both surface and bottom layer of the water column, showed a uni-modal distribution with a dominant peak at coarse fraction, which suggests a characteristic development of floc-sized particles of low mean effective density. On the contrary, the particles supplied toward upstream of Seomjin river from Gwangyang Bay during flood tide showed a bi-modal distribution with a secondary peak at finer fraction, possibly due to the resuspension and the deflocculation associated with the increased shear velocity at near bottom. Break-up of large flocs is also suggested by the increased mean effective density. However, settling velocity was lower during flood tide because of smaller grain size. Thus, net deposition of suspended sediment is expected at within Gwangyang Bay instead of upstream of Seomjin River, even though suspended sediment transport rate at near bottom water was three times higher than that at surface water during flood tide.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.164-164
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• 2016

Radar rainfall estimates have been widely used in calculating rainfall amount approximately and predicting flood risks. The radar rainfall estimates have a number of error sources such as beam blockage and ground clutter hinder their applications to hydrological flood forecasting. Moreover, it has been reported in paper that those errors are inter-correlated spatially and temporally. Therefore, in the current study, we tested influence about spatio-temporal errors in radar rainfall estimates. Spatio-temporal errors were simulated through a stochastic simulation model, called Multivariate Autoregressive (MAR). For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo. The results indicated that spatio-temporal dependent errors caused much higher variations in peak discharge than spatial dependent errors. To further investigate the effect of the magnitude of time correlation among radar errors, different magnitudes of temporal correlations were employed during the rainfall-runoff simulation. The results indicated that strong correlation caused a higher variation in peak discharge. This concluded that the effects on reducing temporal and spatial correlation must be taken in addition to correcting the biases in radar rainfall estimates. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which was funded by the Korea Institute of Construction Technology.