• Journal of Korean Society of Water and Wastewater
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• v.38 no.1
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• pp.1-15
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• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.4
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.6
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• pp.503-512
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• 2020

Identifying the water circulation status is one of the indispensable processes for watershed management in an urban area. Recently, various water circulation models have been developed to simulate the water circulation, but it takes a lot of time and cost to make a water circulation model that could adapt the characteristics of the watershed. This paper aims to develop a water circulation state estimation model that could easily calculate the status of water circulation in an urban watershed by using multiple linear regression analysis. The study watershed is a watershed in Seoul that applied the impermeable area ratio in 1962 and 2000. And, It was divided into 73 watersheds in order to consider changes in water circulation status according to the urban characteristic factors. The input data of the SHER(Similar Hydrologic Element Response) model, a water circulation model, were used as data for the urban characteristic factors of each watershed. A total of seven factors were considered as urban characteristic factors. Those factors included annual precipitation, watershed area, average land-surface slope, impervious surface ratio, coefficient of saturated permeability, hydraulic gradient of groundwater surface, and length of contact line with downstream block. With significance probabilities (or p-values) of 0.05 and below, all five models showed significant results in estimating the water circulation status such as the surface runoff rate and the evapotranspiration rate. The model that was applied all seven urban characteristics factors, can calculate the most similar results such as the existing water circulation model. The water circulation estimation model developed in this study is not only useful to simply estimate the water circulation status of ungauged watersheds but can also provide data for parameter calibration and validation.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.472-472
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• 2023

In this study, the transport of pollutants was analyzed using the K-River and K-DRUM coupling model for water pollution accidents that occurred in the Nakdong River water system. In Korea, the necessity of a distribution model that accommodates the water circulation process and the importance of nonpoint pollution sources were emphasized in water quality management after the introduction of the total amount of water pollution. Therefore, in order to reflect the runoff characteristics of nonpoint sources, the K-DRUM distribution model, which can analyze pollution in the basin, was used. And the reproducibility of the model was improved by applying the operating rules of dams operating in the Nakdong River system. In addition, in order to analyze the movement of pollutants in the river, only the advection part of the advection-dispersion equation was applied to the 1D hydraulic model K-River to perform pollutant tracking. As a result of water pollution analysis, the peak concentration of the pollutant was underestimated, but the arrival time and the trend of the overall pollutant concentration were well reproduced.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.74-83
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• 2007

The goal of this study is to establish an integrated watershed hydrologic model for the whole Nakdong River basin whose area is an approximately 24,000 km2. Including a number of watershed elements such as rainfall, runoff, water use, and so on, the proposed model is based on SWAT model, and is used to improve the flow duration curve estimation of ungauged watersheds for Korean Total Maximum Daily Load (TMDL). The model is also used to recognize quantitatively the river flow variation due to water use elements and large dam effluents in the whole watershed. The established combined watershed hydrologic model, SWAT-Nakdong, is used to evaluate the quantified influences of artificial water balance elements, such as a dam and water use in the watershed. We apply two water balance scenarios in this study: the dam scenario considering effluent conditions of 4 large multi-purpose dams, Andong dam, Imha dam, Namgang dam, and Habcheon dam, and the water use scenario considering a water use for stream line and the effluent from a treatment plant. The two scenarios are used to investigate the impacts on TMDL design flow and flow duration of particular locations in Nakdong River main stream. The results from this study will provide the basic guideline for the natural flow restoration in Nakdong River.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.463-473
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• 2019

This study compared the characteristics of the optimization technique and the water supply and demand forecast using K-WEAP (Korea-Water Evaluation and Planning System) model and MODSIM (Modified SIMYLD) model considering wtaer supply priority. Currently, The national water resources plan applied same priority for municipal, industrial and agricultural demand. the K-WEAP model performs the ratio allocation to satisfy the maximum satisfaction rate, whereas the MODSIM model should be applied to the water supply priority of demands. As a result of applying the priority, water shortage decreased by an average of $1,035,000m^3$ than same prioritized results. It is due to the increase of the return flow rate as the distribution of Municipal and industrial water increases. Comparing the analysis results of K-WEAP and MODSIM applying the priorities, the relative error was within 5.3% and the coefficient of determination ($R^2$) was 0.9999. In addition, if both models provide reasonable water balance analysis results, K-WEAP is superior to GUI convenience for model construction and data processing. However, MODSIM is more effective in simulation time efficiency. It is expected that it will be able to carry out analysis according to various scenarios using the model.

• Magazine of the Korean Society of Agricultural Engineers
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• v.34 no.1
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• pp.87-99
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• 1992

A mathematical model, UNSATR which predicts the seepage flow through the body of dike especially under the tidal fluctuation has been developed. This model has been revised from UNSAT2 model which was developed on the basis of the saturated-unsaturated theory by Neuman. UNSATR has been verified and applied to the hydraulic model in order to estimated the seepage quantity, the formation of free water surface etc. The results lead to the following conclusions : 1. Seepage rates between the mathematical model and hydraulic model experiment are very similar to each other both in constant and transient water level conditions. 2. The lapsed time to be steady state of the free water surface becomes late as the tidal levels are relatively low mainly due to the seepage flow from the unsaturated zone of the body of dike. 3. Under the transient state of water levels, owing to the flow from the unsaturated domain, streamlines crossing to the free water surface are found and time lag during a falling tide may allow the free water surface inside the body of dike to stand at a high level than the outside water level. 4. The utility and validity of UNSATR model are convinced when the analyses on seepage problems through the porous embankment of the soil structures on the conditions of the steady and unsteady states are carried out.

• Korean Journal of Environmental Agriculture
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• v.33 no.1
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• pp.37-43
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• 2014

BACKGROUND: Water quality data are collected less frequently than flow data because of the cost to collect and analyze, while water quality data corresponding to flow data are required to compute pollutant loads or to calibrate other hydrology models. Regression models are applicable to interpolate water quality data corresponding to flow data. METHODS AND RESULTS: A regression model was suggested which is capable to consider flow and time variance, and the regression model coefficients were calibrated using various measured water quality data with genetic-algorithm. Both LOADEST and the regression using genetic-algorithm were evaluated by 19 water quality data sets through calibration and validation. The regression model using genetic-algorithm displayed the similar model behaviors to LOADEST. The load estimates by both LOADEST and the regression model using genetic-algorithm indicated that use of a large proportion of water quality data does not necessarily lead to the load estimates with smaller error to measured load. CONCLUSION: Regression models need to be calibrated and validated before they are used to interpolate pollutant loads, as separating water quality data into two data sets for calibration and validation.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.2
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• pp.23-32
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• 1993

This paper describes the background and the development of a hydrologic network flow model. The model was development to simulate daily water demand and supply for selected stream reaches within a watershed, and used as a tool for evaluating, simulating, and planning a water resources system. The proposed network flow model considers daily runoff from subareas, various water demands, and diversion structures within each subarea. Daily streamflow at a reach is simulated after balancing the water demands from subareas. The lateral inflow from subareas is simulated using a modified tank model. Total water demands consist of the daily demands for agricultural, domestic, industrial, livestock, fishery, and environmental uses within a rural district. The return flow, diversions from sources and storage components such as reservoirs were also incorporated into the mode l . The developed model is a generalized version that may be applied to different combinations of river reaches for a given system. This may help potential users identify areas where water supply does not suffice the demands for different time horizons.