• Water for future
• /
• v.28 no.2
• /
• pp.103-114
• /
• 1995

A stochastic model "STO-RIV" for the prediction of water quality variation in a river system has been developed. Extended Streeter-Phelps equation and Monte Carlo simulation are used in the model. The model is applied to the reach of Waegwan to Mulkeum in the Nakdong River to compute the probability distribution of BOD and DO concentration at Mulkeum site. As the strategies to attain the goal of the water quality, some alternatives considering the treatment effect of the Keumho river are discussed using the stochastic model. Application of stochastic analysis to water quality management is strongly recommended in this country.s country.

• Journal of Korea Water Resources Association
• /
• v.38 no.9 s.158
• /
• pp.791-800
• /
• 2005

An ILLUDAS-NPS model was developed which is able to compute pollutant loadings and the concentrations of water quality constituents. This model is based on the existing ILLUDAS model, and added for use in the water quality analysis process during dry and rainy periods. For dry period, the specifications of coefficients for discharge and water quality were used. During rainfall, we used the daily pollutant accumulation method and the washoff equation for computing water quality each time. According to the results of verification, the ILLUDAS-NPS model provides generally similar outputs with the measured data on total loadings, peak concentration and time of peak concentration for three rainfall events in the Hong-je Basin. In comparison with the SWMM and STORM models, it was shown that there is little difference between ILLUDAS-NPS and SWMM.

• Journal of Korea Water Resources Association
• /
• v.53 no.5
• /
• pp.357-367
• /
• 2020

It is usually not easy to simulate the hydrologic cycle or water quality for ungaged watersheds, especially for long-term simulation. In this paper we evaluated the applicability of GSSHA, a process-based distributed hydrologic model, for the long-term discharge and water quality simulation for the ungaged Peace dam watershed. From the comparative analysis of the simulated discharge and water quality series with measured ones, we concluded that with its overall fair performance on simulating hydrograph patterns of the peak discharges and base flows for major storms the GSSHA model showed some possibility to be used as a watershed model even with its overestimation of peak discharges for small storms and different trends of simulated water quality from measured ones for some periods.

• Journal of Korean Society on Water Environment
• /
• v.34 no.4
• /
• pp.363-374
• /
• 2018

The Han River serves as an important water resource for the city of Seoul, Korea and in the neighboring metropolitan areas. From the Paldang dam to the Jamsil submerged weir, the 4 water intake stations that are located for the Seoul metropolitan population were under review in this study. Therefore the water quality management in this section is very important to monitor, analyze and review to rule out any safety concerns. In this study, a 3-D hydrodynamic model, EFDC (Environmental Fluid Dynamics Code), was applied to the downstream of the Paldang Dam in the Han River, which is about 23 km in length, to determine issues related to water resource management. The 3-D grid was composed of 2,168 horizontal grids and three vertical layers. In this case, the hydrodynamic model was calibrated and verified with an observed average daily water surface elevation, water temperature and flow rate data for 3 years (2013~2015). The developed EFDC model proved to reproduce the hydrodynamics of the Han River well. The composition ratios of the noted incoming flows at the monitored intake stations for 3 years and their flow patterns in the river were analyzed using the validated model. It was found that the flow of the Wangsuk Stream depended on the Paldnag dam discharge, and it was noted that the composition ratios of the stream at the intake stations changed accordingly. In a word, the Wangsuk Stream moved mainly along the right bank of the Han River under the condition of a normal dam flow. As can be seen, when the dam discharge rate was low, the incidence of lateral mixing was often seen. The scenario analyses were also conducted to predict the transport of conservative pollutants as in the case of a chemical spill accident. Generally speaking, when scenarios were applied, the arrival time and concentration of pollutants at each intake station was thus predicted.

• Journal of Korean Society on Water Environment
• /
• v.23 no.2
• /
• pp.260-265
• /
• 2007

This study was carried out to complement water quality standards and to establish new concept for water quality standards reflecting current state of water quality in small streams. By this purpose, discriminant analysis was performed and Water Quality Level Model (WQLM) was developed using the data such as EC, BOD, $COD_{Mn}$, SS, T-N, T-P, $NH_3-N$ in 224 agricultural streams. To give water quality level for water quality parameters, it divided into 20% respectively in the order of excellent water quality. On the basis of the lowest water quality level, water quality level of small streams is granted. As a result of it, number of stream corresponding to Level I was no, Level II was 2 streams, Level III was 22 streams, Level IV was 70 streams, and Level V was 130 streams. Average of water quality in each level was the highest in Level V. EC, SS, and T-N of 7 parameters were selected in variance concerned water quality level. By standardized canonical discriminant function coefficient, EC of three variances was the highest in 0.625 at the discriminant power. The next was T-N (0.509), SS (0.414). By discriminant function for water quality level, Level II was equal to $-2.973+19.376{\times}(EC)+0.647{\times}(T-N)+0.009{\times}(SS)$, Level III was equal to $-3.288+19.190{\times}(EC)+0.733{\times}(T-N)+0.041{\times}(SS)$, Level IV was equal to $-4.462+27.097{\times}(EC)+0.792{\times}(T-N)+0.053{\times}(SS)$, and Level V was equal to $-9.117+40.040{\times}(EC)+1.305{\times}(T-N)+0.111{\times}(SS)$. As a result of test at real agricultural watershed of Jeongan and Euidang in Gongju city, the fitness of WQLM was high to 88.78%. But, to get accomplished water quality assessment more exactly in agricultural streams, we had to concentrate and get vast data, and WQLM was modified and complemented continually.

• Journal of Environmental Impact Assessment
• /
• v.18 no.2
• /
• pp.59-67
• /
• 2009

Water quality of the Lake Youngrang in the Sokcho City is eutrophic. Jangcheon is the largest inflow source to the lake. Major pollutant sources are stormwater runoff from resort areas and various land uses in the Jangcheon watershed. A storm sewer on the southern end of the lake is also an important pollution source. In this study, water quality modelling for Lake Youngrang was carried out considering the rainfall-runoff pollution loads from the watershed. The rainfall-runoff curves and the rainfall-runoff pollutant load curves were derived from the rainfall-runoff survey data during the recent 4 years. The rainfall-runoff pollution loads and flow from the Jangcheon watershed and the storm sewer were estimated using the two kinds of curves, and they were used as the flow and the boundary data of the WASP model. With the measured water quality data of the year 2005 and 2006, WASP model was calibrated. Non-point pollution control measures such as wet pond and infiltration trench were considered as the alternative for water quality management of the lake. The predicted water quality were compared with those under the present condition, and the improvement effect of the lake water quality were analyzed.

• Journal of environmental and Sanitary engineering
• /
• v.15 no.3
• /
• pp.101-119
• /
• 2000

In order to establish water quality management planning in some watershed, water quality of the future of the watershed should be predicted first. The Yongsan river various pollutant sources ; sewage, industry, livestock, farming and so on. And pollutants from these sources are likely to increase even though a number of publicly owned treatment works(POTWs) are founded. Therefore, it is estimated that water quality if the river would be even worse than now in near future. In this study, water quality of the future(2001, 2006) on the Yongsan river was simulated with QUAL2E model. Concentration of three water quality parameters(BOD, T-N, T-P) was predicted according to dry season, low flow season, average flow season of the river with and without POTWs. The results of this study showed the significant contrast in concentration between with and without POTWs, specially in terms of T-N and T-P. Therefore, POTWs must be founded around the Yongsan river and more advanced treatment should be considered. And because these parameters are mostly affected by polluants from upper watershed, including Kwangiudcheon, water quality management planning on the Yongsan river might be focused on this area.

• Korean Journal of Agricultural Science
• /
• v.47 no.4
• /
• pp.1029-1037
• /
• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of Korean Society of Water and Wastewater
• /
• v.28 no.5
• /
• pp.561-571
• /
• 2014

The paper is to estimate willingness-to-pay (WTP) for tap water quality improvement in Busan, using parametric approach in contingent valuation method(CVM). For parametric approach linear logit model and log logit model are employed in double-bounded dichotomous choice format of CVM. For the reliability and the validity of contingent valuation method a survey was conducted for 665 respondents, who were sampled by stratified random sampling method, by personal interview method. The result of mean WTP for the tap water quality improvement in Busan was estimated to be 3,687 won and 3,660 won per month per household, while median WTP being 1,884 won and 1,892 won per month per household, respectively by linear logit model and log logit model. Provided that our sample is broadly representative of the Busan's population, an estimate of the annual aggregated benefit of residential water improvement for all Busan households is approximately 29.7 billion won to 29.8 billion won based on median WTP.

• Journal of Korean Society on Water Environment
• /
• v.29 no.5
• /
• pp.625-629
• /
• 2013

The objective of this study is to develop and evaluate simple and multiple regression models for Total Organic Carbon (TOC) concentration estimation in stream flow. For development (using water quality data in 2012) and evaluation (using water quality data in 2011) of regression models, we used water quality data from downstream of Yeongsan river basin during 2011 and 2012, and correlation analysis between TOC and water quality parameters was conducted. The concentrations of TOC were positively correlated with Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), TN (Total Nitrogen), Water Temperature (WT) and Electric Conductivity (EC). From these results, simple and multiple regression models for TOC estimation were developed as follows : $TOC=0.5809{\times}BOD+3.1557$, $TOC=0.4365{\times}COD+1.3731$. As a result of the application evaluation of the developed regression models, the multiple regression model was found to estimate TOC better than simple regression models.