• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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• pp.37-45
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• 2015

Typically, water quality sampling takes place intermittently since sample collection and following analysis requires substantial cost and efforts. Therefore regression models (or rating curves) are often used to interpolate water quality data. LOADEST has nine regression models to estimate water quality data, and one regression model needs to be selected automatically or manually. The nine regression models in LOADEST and auto-selection by LOADEST were evaluated in the study. Suspended solids data were collected from forty-nine stations from the Water Information System of the Ministry of Environment. Suspended solid data from each station was divided into two groups for calibration and validation. Nash-Stucliffe efficiency (NSE) and coefficient of determination ($R_2$) were used to evaluate estimated suspended solid loads. The regression models numbered 1 and 3 in LOADEST provided higher NSE and $R_2$, compared to the other regression models. The regression modes numbered 2, 5, 6, 8, and 9 in LOADEST provided low NSE. In addition, the regression model selected by LOADEST did not necessarily provide better suspended solid estimations than the other regression models did.

• Journal of Korean Society on Water Environment
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• v.38 no.6
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• pp.306-315
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• 2022

In this study, long-term measurement data were applied to the LOADEST model and used as an analysis tool to identify and interpret trends in pollution load. The LOADEST model is a regression equation-based pollution load estimation program developed by the United States Geological Survey (USGS) to estimate the change in the pollution load of rivers according to flow rate and time and provides 11 regression equations for pollution load evaluation. As a result of simulating the Gwangjuchen2, Pungyeongjeongchen, and Pyeongdongchen in the Yeongbon B unit basin in the middle and upper reaches of the Yeongsan River with the LOADEST model using water quality and flow measurement data, lower values were observed for the Gwangjuchen2 and Pyeongdongchen, whereas the Pungyeongjeongchen had higher values. This was judged to be due to the characteristics of the LOADEST model related to data continuity. According to the parameters estimated by the LOADEST model, pollutant trends were affected by increases in the flow. In addition, variability increased with time, and BOD and T-P were affected by the season. Thus, the LOADEST model can contribute to water quality management as an analytical tool for long-term data monitoring.

• Journal of Korean Society on Water Environment
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• v.25 no.2
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• pp.212-220
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• 2009

In many studies, the Numeric Integration (NI) method has been widely used to calculate pollutant loads from the watershed because it is easy to apply. However, there have been many needs for more accurate pollutant loads estimation method with the restricted number of water quality samples. However, the ESTIMATOR model does not allow the users to define the regression model to explain the measured flow and water quality relationship, indicating the ESTIMATOR model is not flexible. The LOADEST model allows the user to choose the model type from 11 predefined general forms of regression equations. Annual loads of T-N and T-P with the LOADEST model were 0.70 times and 0.84 times of those by NI method, respectively. The coefficient of determination ($R^2$) of the LOADEST regression for the T-N and T-P were 0.92 and 0.72, respectively. This indicates that the load estimation regression model with the LOADEST for the study watershed explains the relationship between the observed flow and water quality data well reasonably well. Based on these findings, we suggest that the LOADEST model estimated regression equation could be used to estimate pollutant loads using the measured flow data for the study watershed.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.448-448
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• 2018

In this study, the regression models (Load ESTimator and eight-parameter model) were evaluated to estimate instantaneous pollutant loads under various criteria and optimization methods. As shown in the results, LOADEST commonly used in interpolating pollutant loads could not necessarily provide the best results with the automatic selected regression model. It is inferred that the various regression models in LOADEST need to be considered to find the best solution based on the characteristics of watersheds applied. The recently developed eight-parameter model integrated with Genetic Algorithm (GA) and Gradient Descent Method (GDM) were also compared with LOADEST indicating that the eight-parameter model performed better than LOADEST, but it showed different behaviors in calibration and validation. The eight-parameter model with GDM could reproduce the nitrogen loads properly outside of calibration period (validation). Furthermore, the accuracy and precision of model estimations were evaluated using various criteria (e.g., $R^2$ and gradient and constant of linear regression line). The results showed higher precisions with the $R^2$ values closed to 1.0 in LOADEST and better accuracy with the constants (in linear regression line) closed to 0.0 in the eight-parameter model with GDM. In hence, based on these finding we recommend that users need to evaluate the regression models under various criteria and calibration methods to provide the more accurate and precise results for pollutant load estimations.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.288-288
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• 2016

유역단위 오염부하량 산정에는 SWAT, HSPF 등의 물리적 매개변수 기반 분포형 모형이 주로 사용되고 있으나, 공간분포형 입력자료로 인한 많은 매개변수는 모의 과정을 복잡하게 하며, 보정 과정에 있어 많은 시간과 노력을 요구하는 단점이 있다. 이로 인해 실무에서는 원단위법이나 유량-부하량 관계식과 같은 통계적 분석에 의한 회귀식이 주로 사용되고 있다. 그 중 LOADEST는 회귀식 기반 프로그램으로, 다양한 연구자들에 의해 연구되고 있으나, 수질 모형과의 모의능력을 비교하는 연구는 부족하다. 본 연구에서는 청미천 상류유역을 대상으로 유역특성에 따른 LOADEST 기반 회귀식의 매개변수를 추정하여 오염부하량을 모의하고, SWAT 모형에 의한 오염부하량 모의결과와 비교 평가하고자 한다. 모형의 구동 및 회귀식 매개변수 추정에 필요한 입력 자료는 용인시 백암면 일대에서 2013년부터 2015년까지 모니터링한 수질, 유량 및 기상자료와 지형자료 (토지이용도, 토양도, 수치표고자료)를 이용하여 구축하였다. LOADEST 기반 회귀식의 매개 변수 추정은 김계웅 (2015)이 개발한 방법을 사용하였으며, 유역면적, 토지이용비율 등은 지형자료를 이용하여 산정하였다. SWAT 모형의 보정은 2013년부터 2014년까지의 자료를 이용하였으며, 2015년 자료를 이용하여 검정하였다. 본 연구의 결과는 비점오염원 모델에 대한 이해를 넓히고, 오염부하량 모의를 위한 모형 선정에 있어 도움이 될 수 있을 것으로 기대한다.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.151-163
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• 2018

The objective of this study was to estimate LOADEST (LOAD Estimator) coefficients for simulating pollutant loads in ungauged watersheds. Regression models of LOADEST were used to simulate pollutant loads, and the multiple linear regression (MLR) was used for coefficients estimation on watershed characteristics. The fifth and third model of LOADEST were selected to simulate T-N (Total-Nitrogen) and T-P (Total-Phosphorous) loads, respectively. The results and statistics indicated that regression models based on LOADEST simulated pollutant loads reasonably and model coefficients were reliable. However, the results also indicated that LOADEST underestimated pollutant loads and had a bias. For this reason, simulated loads were corrected the bias by a quantile mapping method in this study. Corrected loads indicated that the bias correction was effective. Using multiple regression analysis, a coefficient estimation methods according to the watershed characteristic were developed. Coefficients which calculated by MLR were used in models. The simulated result and statistics indicated that MLR estimated the model coefficients reasonably. Regression models developed in this study would help simulate pollutant loads for ungauged watersheds and be a screen model for policy decision.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.97-107
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• 2017

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.3
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• pp.347-357
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• 2015

Baswflow is defined as short term discharge through groundwater caused by rainfall events. Impacts of baseflow is significant on water quality especially where pervious agricultural watershed as groundwater is more vulnerable to the contamination. In this study, the Cheongmicheon watershed was subjected to study to assess the impacts of baseflow on surface water quality, where more than 90% of pollutant load is originated from the livestock raising area, and very high probability of surface water contamination due to the baseflow. To estimate nutrient loading cased by baseflow, NI (Numerical Integration) model and LOADEST (LOADing ESTimation) model were used.

• Journal of Soil and Groundwater Environment
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• v.25 no.3
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• pp.52-64
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• 2020

The universal soil loss equation (USLE), a model for estimating the potential soil loss, has been used not only in research areas but also in establishing national policies in South Korea. Despite its wide applicability, USLE cannot adequately address the effect of seasonal variances. To overcome this limit, the ArcGIS-based Sediment Assessment Tool for Effective Erosion (ArcSATEEC) has been developed as an alternative model. Although the field-scale (< 100 ㎡) application of this model produced reliable estimation results, it is still challenging to validate accuracy of the model estimation because it only estimates potential soil losses, not the actual sediment yield. Therefore, in this study, a method for estimating actual soil loss based on the ArcSATEEC model was suggested. The model was applied to eight watersheds in South Korea to estimate sediment yields. Correction factor was introduced for each watershed, and the estimated sediment yield was compared with that of the estimated yield by LOAD ESTimator (LOADEST). Sediment yield estimation for all watersheds exhibited reliable results, and the validity of the proposed correction factor was confirmed, suggesting the correction factor needs to be considered in estimating actual soil loss.