• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1798-1802
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation.The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall.The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.

• Journal of Industrial Technology
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• v.27 no.A
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• pp.95-102
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation. The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall. The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.4
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• pp.76-86
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• 2000

The objective function of reservoir storage error was suggested to simulate daily reservoir inflow. DAWAST model, UMAX, LMAX, FC,CP, CE were calibrated. Daily reservoir inflow was imulated with calibrated parameters and reservoir storage was simulated on a daily basis. The simulated results were compared with the monthly results by Gajiyama equation and ten-day results by Tank rainfall-runoff model through equal value lines and hydrographs . DAWAST model showed the best results compared with Gajiymama equation and Tank model. Especially, DAWAST model showed a good agreement in dry periods. NEW concept using objective function of storage error was believed to be satisfactory and to be applied in estimating reservoir inflow.

• 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 the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.157-169
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• 2010

In this study, the applicability of SRTM(The Shuttle Radar Topography Mission) DEM(Digital Elevation Model) which is one of the remotely sensed shuttle's radar digital elevation was tested for use as the input data in a grid-based rainfall-runoff model. The SRTM DEM and digital topographic map derived DEM(TOPO DEM) were building with 500m spatial resolution for the Chungju-Dam watershed which located in the middle east of South Korea, and stream-burning method was applied to delineate the proper flow direction for model application. Similar topographical characteristics were shown as a result of comparing elevation, flow-direction, hydrological slope, number of watershed cell, and profile between SRTM DEM and TOPO DEM. Two DEMs were tested by using a grid-based rainfall-runoff model named KIMSTORM with 6 storm events. The results also showed no significant differences in average values of relative error for both peak runoff(0.91 %) and total runoff volume(0.29 %). The results showed that the SRTM DEM has applicability like TOPO DEM for use in a grid-based rainfall-runoff modeling.

• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.581-593
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• 2015

This study constructs the drought outlook system using ESP(Ensemble Streamflow Prediction) method and evaluates its utilization for drought prediction. Historical Runoff(HR) was estimated by employing LSM(Land Surface Model) and the observed meteorological, hydrological and topographical data in South Korea. Also Predicted Runoff(PR) was produced for different lead times(i.e. 1-, 2-, 3-month) using 30-year past meteorological data and the initial soil moisture condition. The HR accuracy was higher during MAM, DJF than JJA, SON, and the prediction accuracy was highly decreased after 1 month outlook. SRI(Standardized Runoff Index) verified for the feasibility of domestic drought analysis was used for drought outlook, and PR_SRI was evaluated. The accuracy of PR_SRI with lead times of 1- and 2-month was highly increased as it considered the accumulated 1- and 2-month HR, respectively. The Correlation Coefficient(CC) was 0.71, 0.48, 0.00, and Root Mean Square Error(RMSE) was 0.46, 0.76, 1.01 for 1-, 2- and 3-month lead times, respectively, and the accuracy was higher in arid season. It is concluded that ESP method is applicable to domestic drought prediction up to 1- and 2-month lead times.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1061-1072
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• 2005

A hydrological grey model is developed to forecast short-term river runoff from the Naju watershed located at upstream of the Youngsan estuary dam in Korea. The runoff of the Naju watershed is measured in real time at the Naju streamflow gauge station, which is a key station for forecasting the upstream inflow and operating the gates of the estuary dam in flood period. The model's governing equation is formulated on the basis of the grey system theory. The model parameters are reparameterized in combination with the grey system parameters and estimated with the annealing-simplex method In conjunction with an objective function, HMLE. To forecast accurately runoff, the fifth order differential equation was adopted as the governing equation of the model in consideration of the statistic values between the observed and forecast runoff. In calibration, RMSE values between the observed and simulated runoff of two and six Hours ahead using the model range from 3.1 to 290.5 $m^{3}/s,\;R^2$ values range from 0.909 to 0.999. In verification, RMSE values range from 26.4 to 147.4 $m^{3}/s,\;R^2$ values range from 0.940 to 0.998, compared to the observed data. In forecasting runoff in real time, the relative error values with lead-time and river stage range from -23.4 to $14.3\%$ and increase as the lead time increases. The results in this study demonstrate that the proposed model can reasonably and efficiently forecast runoff for one to six Hours ahead.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.79-90
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• 2006

The partitioning level of a catchment becomes an issue if the calculated results from different levels show the same performance regardless of the levels. This study aims to identify the proper processing level of spatial resolution for the SWMM model application in an urban area. Using GIS overlaying technique, the division of subcatchments as a hydrologic similarity unit (HSU) is achieved with a comprehensive consideration of surface slope conditions, flow directions of storm sewers, and current land cover situation. Three surface-sewer alternatives are made on the basis of three different levels of surface divisions as well as the number of sewer connections and used as runoff simulation fields for the application of SWMM. As the result, it is found that the effect of a spatial resolution on the surface runoff results is not significant. On the other hand, the accumulated pollution load from an unit subcatchment, which is built by aggregation of several unit subcatchments consisting of various land cover conditions is reduced through the deterioration of surface spatial resolution. Although overall runoff pattern and accumulated runoff are little affected by spatial resolution, the simulated runoff from sewer outlet shows slight difference at the peak appearance time. The gap between surface pollution load accumulated and it discharged from the sewer outlet in a surface-sewer alternative during runoff period is monitored but the level of error is less than 5-10% except the lowest spatial resolution case.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.261-274
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• 2006

This study performs a daily long-term runoff analysis for 30 years to forecast medium- and long-term probabilistic reservoir inflows on the Soyang River basin. Snowmelt is computed by Anderson's temperature index snowmelt model and potenetial evaporation is estimated by Penman-combination method to produce input data for a rainfall-runoff model. A semi-distributed TOPMODEL which is composed of hydrologic rainfall-runoff process on the headwater-catchment scale based on the original TOPMODEL and a hydraulic flow routing model to route the catchment outflows using by kinematic wave scheme is used in this study It can be observed that the time variations of the computed snowmelt and potential evaporation are well agreed with indirect observed data such as maximum snow depth and small pan evaporation. Model parameters are calibrated with low-flow(1979), medium-flow(1999), and high-flow(1990) rainfall-runoff events. In the model evaluation, relative volumetric error and correlation coefficient between observed and computed flows are computed to 5.64% and 0.91, respectively. Also, the relative volumetric errors decrease to 17% and 4% during March and April with or without the snowmelt model. It is concluded that the semi-distributed TOPMODEL has well performance and the snowmelt effects for the long-term runoff computation are important on the study area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.493-503
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• 2014

The NRCS-CN method is generally applied for estimating effective rainfalls in practice, in which the basin-averaged CN is normally used. In order to develop a more appropriate method for estimating effective rainfalls in a basin, this study compared estimated effective rainfalls from two distinct methods with the observed direct runoff. The first method is to estimate the basin-representative effective rainfall using the basin-averaged CN (hereafter, effective rainfall I), whereas the second method to estimate the basin-averaged effective rainfall through areal-averaging sub-area effective rainfalls corresponding to the soil type and landuse type (hereafter, effective rainfall II). The overall results indicated that the effective rainfall II was higher than the effective rainfall I and closer to the observed direct runoff. The study also performed error analyses to verify that the effective rainfall II can be applied in practice in a basin as more accurate estimate of basin-representative effective rainfall.