• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.965-977
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.6
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• pp.3-12
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• 2008

It is important to estimate accurate effective rainfall to analyse flood flow and long-term runoff for the rational planning, design, and management of water resource. The initial abstraction is also important to estimate effective rainfall. The Soil Conservation Service (SCS) has developed a procedure and it has been most commonly applied to estimate effective rainfall. But the SCS method still has weak points, because of unnatural assumptions such as antecedent moisture conditions and initial abstraction. The coefficient of initial abstraction(K) is depending on the soil moisture condition and antecedent rainfall. The maximum storage capacity of Umax which is calibrated by stream flow data in the proposed watershed was derived from the DAWAST(DAily WAtershed STreamflow) model. The values of K obtained from 69 storm events at the five watersheds are ranging from 0.133 to 0.365 and its mean value is 0.207. Effective rainfall could be estimated more reasonably by introducing new concept of initial abstraction. The equation of $K=0.076Sa^{0.255}$ was recommended instead of 0.2 and it could be applicable to the small-medium rural watersheds.

• Journal of Environmental Impact Assessment
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• v.9 no.3
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• pp.239-248
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• 2000

The aggravating water quality from the expansion of industrialization along with increasing population lead to develop more intensive physical measures to secure better drinking water quality. This study was mainly initiated to establish a water-pollutant buffering zone for the upper stream basin of Paldang--the major source area of drinking water for the metropolitan Seoul and suburban areas with a population more than 13 million. Two different criteria were considered in determining the buffering distance from the edge of the streamflow : 1km-width buffer zone for the special protection area which has been strictly controlled by the conventional laws for the protection of drinking water supply, and 500m-width buffer zone for the rest of the area. To delineate the exact boundaries of the water-pollutant buffering zone, GIS database was created integrating topography, hydrography, cadastral, and other related layers. The newly designated water-pollutant buffering zone would contribute to improve the water quality in a long term along with the conservation of the wet land. More study, however, should be made within the water-pollutant buffering zone such as the detailed survey of the pollutants, vegetation, and ecosystem for more effective management of the buffering zone.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.1
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• pp.27-38
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• 2006

In this study, we developed a program to estimate discharge efficiently considering major hydraulic characteristic including water level, river bed, water slope and roughness coefficient in a natural river. Stream discharge was measured at Gongju gauge station located in the down stream of the Daechung Dam during normal and dry seasons from 2003 to 2004. The developed model was compared with the results from the existing rating curve at T/M gage stations, and was used for runoff analyses. Evaluating the developed river discharge estimation program, it was applied during 1983-2004 that base flow separation method and RRFS (Rainfall Runoff Forecasting System) which is based on SSARR (Streamflow Synthesis And Resevoir Regulation). The result presents the stage-discharge curve creator range at the Gong-ju is overestimated by approximately $10-20\%$, especially at the low stage. It is attributed to the hydraulic characteristics at the study. The discharge simulated by the RRFS and base flow separation, which is calibrated using the measurement at the early spring and late fall season during relatively d]v season, shows the least errors. The coefficient of roughness at Gongju station varied with the high and low water level.

• Journal of Environmental Science International
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• v.20 no.11
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• pp.1457-1464
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• 2011

In this study, the method of estimating hydrologic information (water depth, submerged period etc.) on the proper selection of construction point and scale as well as vegetation type suggested for the design of natural riparian rehabilitation structure. Long-term comprehensive watershed model SWAT-K(Korea) was applied to this purpose. Flow duration analysis was conducted to analyze the hydrologic characteristics of Pyungchang watershed at which the 'bangtul' construction method was tested. For this purpose 20 years (1989-2008) rainfall runoff analysis was carried out. Based on the simulated daily streamflow data, flow duration curve was made to analyze the flow characteristics, and the water depth hydrograph was made to analyze the water depth distribution at the cross section. Finally, the information for the selection of proper vegetation according to the submerged period is suggested.

• Journal of Korean Society of Rural Planning
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• v.11 no.1 s.26
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• pp.17-24
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• 2005

The purpose of this study is to evaluate the hydrological impact due to temporal land cover change by gradual urbanization of Anseong-cheon Gongdo watershed ($371.8km^2$). Land covers of 1981, 1990, 2000 Landsat TM images were classified by maximum likelihood method. The watershed showed a trend that forest & paddy areas decreased about $33km^2$ and $27km^2$, respectively and urban area increased about $11km^2$ during the periods. To identify the impact of streamflow due to urbanization, WMS HEC-1 was used. According to apply Huffs quartile storm events by changing land cover data, peak runoff discharge of each frequency rainfall (50, 100, 500 years) increased about 56, 36, $192m^3/sec$, respectively.

• Journal of Korean Society of Forest Science
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• v.85 no.4
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• pp.634-646
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• 1996

This study was carried out to reveal the forest land effect on water purification in mountainous watersheds. Rainfall, throughfall, stemflow, soil and stream water were monitored by pH, electrical conductivity(EC), and dissolved oxygen(DO) in Daehan-Ri and Parkdal-Ri catchments. The results were summarized as follows; 1. Rainfall pH values of Parkdal-Ri and Daehan-Ri were 7.6 and 6.4, respectively. 2. Comparing stemflow and throughfall of Pinus densiflora with Pinus rigida, the pH values of Pinus densiflora were 4.32 and 4.22 and the pH of Pinus rigida were 3.34 and 4.81, respectively. The EC values of Pinus densiflora were $119.7{\mu}S/cm$ and $96.8{\mu}S/cm$ and EC of Pinus rigida were $230.0{\mu}S/cm$ and $82.0{\mu}S/cm$. 3. All pH values were decreased as the streamflow increased except long-term runoff in Daehan-Ri. The EC values also were increased as the streamflow increased, but EC of short-term runoff in Daehan-Ri was gradually decreased as the streamflow increased due to entrance of throughfall which has high EC values at the beginning of rainfall events. The DO concentrations of all experimental plots were elevated as the streamflow increased, because reaeration occurs at the surface of the stream as the increased discharge make turbulence. 4. pH of Stemflow and throughfall in Pinus densiflora were lower than in Quercus acutissima, but EC values were higher in Pinus densiflora. 5. Water purification was mostly influenced by forest soil in forest hydrological processes. 6. Stemflow and throughfall were more influenced by dry deposition and organic acid in crown and bark than those of wet deposition. During the stemflow and throughfall passed forest soil, these acidic stemflow and throughfall were neutralized, and stream water quality was neutral or slightly alkaline.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.