• Korean Journal of Ecology and Environment
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• v.41 no.1
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• pp.43-53
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• 2008

This study was conducted to simulate storm hydrographs on a small forested watershed using TOPMODEL, which is a distributed hydrological model. The Myeongseong watershed, which is 58.3 ha in size, was selected to monitor rainfall and runoff data. The Monte Carlo simulation was also used to calibrate parameters of TOPMODEL. Six rainfall-runoff pairs collected at the watershed in the year 1997 were used for parameter calibration, and eight rainfall-runoff pairs collected during the period of $1998\sim1999$ were used for validation effort. The errors of runoff volume ranged from -2.74% to 1.81%, and an average value of model efficiency in terms of runoff volume was 0.92 for the calibration period. The average value of observed peak discharge was $0.324m^3\;s^{-1}$ for six rainfall-runoff pairs, while the prediction value was $0.295m^3\;s^{-1}$. The simulation errors of peak discharge varied according to rainfall characteristics and antecedent condition, within ranges of -27.65% to -1.13%. The model efficiency for the validation period was 0.92. For the validation period, observed peak discharges have an average value of $0.087m^3\;s^{-1}$ and average value of simulated peak discharge was $0.090m^3\;s^{-1}$. Observed and simulated values of time to peak for the calibration period were 18.3 hrs and 11.0 hrs, respectively, and 16.6 hrs and 13.5 hrs, respectively, for the validation period.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1217-1227
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• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.

• Journal of Korea Water Resources Association
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• v.34 no.3
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• pp.275-285
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• 2001

Since the majority of streamflow during dry periods is provided by groundwater storage, the streamflow depends on a basin moisture state recharged from rainfall during wet periods. This hydrologic characteristics dives good condition to predict long-term streamflow if the basin state like groundwater recharge is known in advance. The objective of this study is to examine groundwater recharge effect to monthly streamflow, and to attempt monthly streamflow prediction using estimated groundwater recharge. The ground water recharge is used as an independent variable with streamflow and precipitation to construct multiple regression models for the prediction. Correlation analysis was performed to assess the effect of groundwater carry-over to streamflow and to establish the associations among independent variables. The predicted streamflow shows that the multiple regression model involved groundwater recharge gives improved results comparing to the model only using streamflow and precipitation as independent variables. In addition, this paper shows that the prediction model with the effect of groundwater carry-over taken into account can be developed using only precipitation.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.471-483
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• 2020

Flood prediction is an important issue to prevent damages by flood inundation caused by increasing high-intensity rainfall with climate change. In recent years, machine learning algorithms have been receiving attention in many scientific fields including hydrology, water resources, natural hazards, etc. The performance of a machine learning algorithm was investigated to predict the water elevation of a river in this study. The aim of this study was to develop a new method for securing a large enough lead time for flood defenses by predicting river water elevation using the a long- short-term memory (LSTM) technique. The water elevation data at the Oisong gauging station were selected to evaluate its applicability. The test data were the water elevation data measured by K-water from 15 February 2013 to 26 August 2018, approximately 5 years 6 months, at 1 hour intervals. To investigate the predictability of the data in terms of the data characteristics and the lead time of the prediction data, the data were divided into the same interval data (group-A) and time average data (group-B) set. Next, the predictability was evaluated by constructing a total of 36 cases. Based on the results, group-A had a more stable water elevation prediction skill compared to group-B with a lead time from 1 to 6 h. Thus, the LSTM technique using only measured water elevation data can be used for securing the appropriate lead time for flood defense in a river.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.313-322
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• 2020

There is always a risk of water disasters due to sudden storms in mountainous regions in Korea, which is more than 70% of the country's land. In this study, a radar-based risk prediction technique for sudden downpour is applied in the mountainous region and is evaluated for its applicability using Mt. Biseul rain radar. Eight local heavy rain events in mountain regions are selected and the information was calculated such as early detection of cumulonimbus convective cells, automatic detection of convective cells, and risk index of detected convective cells using the three-dimensional radar reflectivity, rainfall intensity, and doppler wind speed. As a result, it was possible to confirm the initial detection timing and location of convective cells that may develop as a localized heavy rain, and the magnitude and location of the risk determined according to whether or not vortices were generated. In particular, it was confirmed that the ground rain gauge network has limitations in detecting heavy rains that develop locally in a narrow area. Besides, it is possible to secure a time of at least 10 minutes to a maximum of 65 minutes until the maximum rainfall intensity occurs at the time of obtaining the risk information. Therefore, it would be useful as information to prevent flash flooding disaster and marooned accidents caused by heavy rain in the mountainous area using this technique.

• Korean Journal of Environmental Agriculture
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• v.30 no.3
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• pp.268-274
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• 2011

BACKGROUND: A large scale of sediment load delivered from watershed causes substantial waterway damages and water quality degradation. Controlling sediment loading requires the knowledge of the soil erosion and sedimentation. The various factors such as watershed size, slope, climate, land use may affect sediment delivery processes. Traditionally sediment delivery ratio prediction equations have been developed by relating watershed characteristics to measured sediment yield divided by predicted gross erosion. However, sediment prediction equations have been developed for only a few regions because of limited sediment data. Besides, little research has been done on the prediction of sediment delivery ratio for asia monsoon period in mountainous watershed. METHODS AND RESULTS: In this study Tank model was expanded and applied for estimating sediment yield to Oship River of east coast. The rainfall-runoff in 2006 was verified using the Tank model and we derived good result between observed and calculated discharge in 2009 at the same conditions. In relation to sediment yield, the sediment delivery rate of 2006 was very high than 2009 regardless of methods for estimating sediment load. It was thought to be affected by heavy rainfall due to the typhoon. CONCLUSION(s): For estimating sediment volume from watershed, long-term monitoring data on discharge and sediment is needed. This model will be able to apply to predict discharge and sediment yield simultaneously in ungauged area. This approach is more effective and less expensive method than the traditional method which needs a lot of data collection.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.425-432
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• 2011

To control non-point source pollution at a watershed scale, rainfall-runoff characteristics from forest watersheds should be investigated since the forest is the dominant land use in Korea. Long-term monitoring would be an ideal method. However, computer models have been utilized due to limitations in cost and labor in performing long-term monitoring at the watersheds. In this study, the Geo-spatial interface to the Water Erosion Prediction Project (GeoWEPP) model was evaluated for its runoff prediction from a coniferous forest dominant watersheds. The $R^2$ and the NSE for calibrated result comparisons were 0.77 and 0.63, validated result comparisons were 0.92, 0.89, respectively. These comparisons indicated that the GeoWEPP model can be used in evaluating rainfall-runoff characteristics. To estimate runoff changes from a coniferous forest watershed with various cover degree scenarios, ten cover degree scenarios (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%) were run using the calibrated GeoWEPP model. It was found that runoff increases with decrease in cover degree. Runoff volume was the highest ($206,218.66m^3$) at 10% cover degree, whereas the lowest ($134,074.58m^3$) at 100% cover degree due to changes in evapotranspiration under various cover degrees at the forest. As shown in this study, GeoWEPP model could be efficiently used to investigate runoff characteristics from the coniferous forest watershed and effects of various cover degree scenarios on runoff generation.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.547-565
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• 2011

Different types of schemes have been used in stage prediction involving conceptual and physical models. Nevertheless, none of these schemes can be considered as a single superior model. To overcome disadvantages of existing physics based rainfall-runoff models for stage predicting because of the complexity of the hydrological process, recently the data-derived models has been widely adopted for predicting flood stage. The objective of this study is to evaluate model performance for stage prediction of the Neuro-Fuzzy and regression analysis stage prediction models in these data-derived methods. The proposed models are applied to the Wangsukcheon in Han river watershed. To evaluate the performance of the proposed models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient(NSEC), mean absolute error(MAE), adjusted coefficient of determination($R^{*2}$). The results show that the Neuro-Fuzzy stage prediction model can carry out the river flood stage prediction more accurately than the regression analysis stage prediction model. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.1
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• pp.66-72
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• 2004

In this study, quantity and quality of collected rainwater by sand, gravel, soil, lawn and concrete surface, as collection materials were investigated and Rainwater Collection Prediction Model was developed to predict the amount of collected rainwater. The quantity of collected rainwater in concrete surface, gravel, sand, soil and lawn collection system was 1,067L(93.2%), 1,006L(87.8%), 902L(78.8%), 800L(69.9%), 788.5L(68.8%) for 8 months period, respectively. The average turbidity of collected rainwater in concrete surface, gravel, sand, soil and lawn collection system was 3.2NTU, 2.2NTU, 1.9NTU, 1.7NTU, 1.5NTU for 8 months period, respectively. For sand collection material, predicted amount by the Model and actual collected amount were 931.5L and 902L, which were very closed. For gravel collection material, predicted amount by Model and actual collected amount were 1,028.21. and 1,006L, which were very closed. To simulate the optimal rainwater storage volume, the rainfall and evaporation data in Dae-jeon city were used. For sand collection system with 30m2 area, the maximum storage volume was $17m^3$ and 62% of the year was secured for use of 240L/day.