• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.175-180
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• 2000

The purposes of this study were to determine an algorithm for estimating agricultural water demand of remote sites using remote sensing data and to apply it to Hwanghae South Province and estimate the present and potential water demand for agriculture use. 3 Landsat-5 TM images and DEM(100${\times}$100mm) were used for classification of the existing land cover and land suitability analysis for paddy fields. Also, 20 years meteorological data of North Korea were used for calculating the potential evapotranspiration by Blaney-Criddle eq. and net water demand. The results showed that the present and potential agricultural water demand and the developable area for paddy fields is about 89,300㏊.

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

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.423-426
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• 2003

The purpose of this paper is to estimate Agricultural water demand at irrigation area of sumjin reservoir, the Dongjin River basin, which consist of multi-wide water supply system and complicated irrigation channel and supplementary irrigation facilities.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.201-210
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• 2010

In this study, new methodology to estimate the reliability of a water distribution system using HSPDA model is suggested. In general, the reliability of a water distribution system can be determined by estimating either the ratio of the required demand to the available demand or the ratio of the number of nodes with sufficient pressure head to the number of nodes with insufficient pressure head when the abnormal operating condition occurs. To perform this approach, hydraulic analysis under the abnormal operating condition is essential. However, if the Demand-Driven Analysis (DDA) which is dependant on the assumption that the required demand at a demand node is always satisfied regardless of actual nodal pressure head is used to estimate the reliability of a water distribution system, the reliability may be underestimated due to the defect of the DDA. Therefore, it is necessary to apply the Pressure-Driven Analysis (PDA) having a different assumption to the DDA's which is that available nodal demand is proportion to nodal pressure head. However, because previous study used a semi-PDA model and the PDA model which had limited applicability depending on the characteristics of a network, proper estimation of the reliability of a water distribution system was impossible. Thus, in this study, a new methodology is suggested by using HSPDA model which can overcome weak points of existing PDA model and Available Demand Fraction (ADF) index to estimate the reliability. The HSPDA can simulate the hydraulic condition of a water distribution system under abnormal operating condition and based on the hydraulic condition simulated, ADF index at each node is calculated to quantify the reliability of a water distribution system. The suggested model is applied to sample networks and the results are compared with those of existing method to demonstrate its applicability.

• Journal of Korea Water Resources Association
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• v.30 no.1
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• pp.45-54
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• 1997

The correct estimation of the daily or hourly urban water demand is required for the efficient management and operation of the water supply facilities. The prediction of water supply demand are regression model and time series method, the optimum ARIMA (Auto Regressive Integrated Moving Average) model was sought for the daily urban water demand estimation in this paper. The data used for this study were obtained from the city of Kwangju Korea. The raw data used in this study were rearranged 15, 30, 60, 90 days for the purpose of analysis. The statistical analysis was applied to the data to obtain the ARIMA model. As a result, the parameters determining the ARIMA model was obtained. The accuracy of the model was 2% of water supply. The developed model was found to be useful for the practical operation and management of the water supply facilities.

• Journal of Soil and Groundwater Environment
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• v.23 no.1
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• pp.92-105
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• 2018

In this study, the projected land use area in 2030 for major crop production was estimated in Jeju Island using land cover map, and corresponding agricultural water demand for 40 sub-regions was quantitatively assessed using the future climate change scenario (RCP 4.5). Estimated basic unit of water demand in 2030 was the highest in the western region, and the lowest in the eastern region. Monthly maximum agricultural water demand analysis revealed that water demand in August of 2030 substantially increased, suggesting the climate of Jeju Island is changing to a subtropical climate in 2030. Agricultural water demand for sub-region in 2030 was calculated by multiplying the target area of the water supply excluding the area not in use in winter season by the basic unit of water demand, and the maximum and minimum values were estimated to be $306,626m^3/day$ at Seogwipo downtown region and $77,967m^3/day$ at Hallim region, respectively. Consequently, total agricultural water demand in Jeju Island in 2030 was estimated to be $1,848,010m^3/day$.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.363-373
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• 2023

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea's SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

• Journal of Environmental Science International
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• v.22 no.5
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• pp.639-649
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• 2013

Over 96.2% of the agricultural water in Jeju Island is obtained from groundwater and there are quite distinct characteristics of agricultural water demand/supply spatially because of regional and seasonal differences in cropping system and rainfall amount. Land use for cultivating crops is expected to decrease 7.4% (4,215 ha) in 2020 compared to 2010, while market garden including various vegetable crop types having high water demand is increasing over the Island, especially western area having lower rainfall amount compared to southern area. On the other hand, land use for fruit including citrus and mandarin having low water demand is widely distributed over southern and northern part having higher rainfall amount. The agricultural water demand of $1,214{\times}10^3\;m^3/day$ in 2020 is estimated about 1.39 times compared to groundwater supply capacity of $874{\times}10^3\;m^3/day$ in 2010 with 42.4% of eastern, 103.1% of western, 61.9% of southern, and 77.0% of northern region. Moreover, net secured amount of agricultural groundwater would be expected to be much smaller due to regional disparity of water demand/supply, the lack of linkage system between the agricultural water supply facilities, and high percentage of private wells. Therefore, it is necessary to ensure the total net secured amount of agricultural groundwater to overcome the expected regional discrepancy of water demand and supply by establishing policy alternative of regional water supply plan over the Island, including linkage system between wells, water tank enlargement, private wells maintenance and public wells development, and continuous enlargement of rainwater utilization facilities.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.4
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• pp.481-489
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• 2009

The objective of this research is to provide more reliable and accurate unit factor of water amount by investigating of informations related to various educational institutions such as elementary, middle, high schools and university. In order to estimate the water demand of educational institutions, first of all, the informations such as building area, site area, total school population, and water amount of various educational institutions are investigated to estimate the water unit factor. In this research, we used the total population of students and teachers to estimate the water demand of educational institutions. The results of unit factors of this research are as follows: 1) The elementary school is $0.027983m^3/person{\cdot}day$, 2) middle school: $0.024106m^3/person{\cdot}day$, 3) high school: $0.041415m^3/person{\cdot}day$, 4) specialized high school (science high school and foreign language high school): $0.156938m^3/person{\cdot}day$ and 5) university: $0.033766m^3/person{\cdot}day$. Finally, these water amounts calculated by unit factors were compared with real water amount of educational institutions.