• Water Engineering Research
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• v.5 no.3
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• pp.123-131
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• 2004

This paper describes the use of spatial interpolation for estimating seasonal crop potential evapotranspiration (PET) and irrigation water requirement in unmeasured evaporation gage stations within Edwards Aquifer, Texas using GIS. The Edwards Aquifer area has insufficient data with short observed records and rare gage stations, then, the investigation of data for determining of irrigation water requirement is difficult. This research shows that spatial interpolation techniques can be used for creating more accurate PET data in unmeasured region, because PET data are important parameter to estimate irrigation water requirement. Recently, many researchers are investigating intensively these techniques based upon mathematical and statistical theories. Especially, three techniques have well been used: Inverse Distance Weighting (IDW), spline, and kriging (simple, ordinary and universal). In conclusion, the result of this study (Table 1) shows the kriging interpolation technique is found to be the best method for prediction of unmeasured PET in Edwards aquifer, Texas.

• Structural Monitoring and Maintenance
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• v.5 no.3
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• pp.379-390
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• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.649-654
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• 2009

Soil temperature is an important tool in predicting a change of climate and agricultural environment together with the change of atmospheric temperature. In this paper, we examine changing patterns of soil temperature measured in 0.5m under ground from 1932 to 1990 and atmospheric temperature from 1961 to 2008, and derive a relationship between atmospheric temperature and soil temperature. Using this model, we predict unmeasured soil temperature in Daegu area and soil temperature is found to be increasing about $0.028^{\circ}C$per a year. Prediction of soil temperature is an important indicator for climate change in Daegu and will be useful information to help us take precautions for global warming, etc.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.

• Journal of Wetlands Research
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• v.22 no.2
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• pp.106-112
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• 2020

Wetlands play an important function and role in hydrological, environmental, and ecological, aspects of the watershed. Water level in wetlands is essential for various analysis such as for the determination of wetland function and its effects on the environment. Since several wetlands are ungauged, research on wetland water level prediction are uncommon. Therefore, this study developed a water level prediction model using multiple regression analysis, principal component regression analysis, artificial neural network, and DNN to predict wetland water level. Geumjeong-Mountain Wetland located in Yangsan-city, Gyeongsangnam-do province was selected as the target area, and the water level measurement data from April 2017 to July 2018 was used as the dependent variable. On the other hand, hydrological and meteorological data were used as independent variables in the study. As a result of evaluating the predictive power, the water level prediction model using DNN was selected as the final model as it showed an RMSE value of 6.359 and an NRMSE value of 18.91%. This research study is believed to be useful especially as a basic data for the development of wetland maintenance and management techniques using the water level of the existing unmeasured points.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.29-41
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• 2010

In this study, the prediction errors of various spatial interpolation methods used to model values at unmeasured locations was compared and the accuracy of these predictions was evaluated. The root mean square (RMS) was calculated by processing different parameters associated with spatial interpolation by using techniques such as inverse distance weighting, kriging, local polynomial interpolation and radial basis function to known elevation data of the east coastal area under the same condition. As a result, a circular model of simple kriging reached the smallest RMS value. Prediction map using the multiquadric method of a radial basis function was coincident with the spatial distribution obtained by constructing a triangulated irregular network of the study area through the raster mathematics. In addition, better interpolation results can be obtained by setting the optimal power value provided under the selected condition.

• Journal of Korean Society on Water Environment
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• v.39 no.2
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• pp.162-174
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• 2023

In order to prevent water pollution caused by organic matter, Total Organic Carbon(TOC) has been adopted indicator and monitored. TOC can be divided into Dissolved Organic Carbon(DOC) and Particulate Organic Carbon(POC). POC is largely precipitated and removed during stream flow, which making DOC environmentally significant. However, there are lack of studies to define spatio-temporal distributions of DOC in stream affected by various land use. Therefore, it is necessary to estimate the past DOC concentration using other water quality indicators to evaluate status of watershed management. In this study, DOC was estimated by correlation and regression analysis using three different organic matter indicators monitored in mixed land-use watersheds. The results of correlation analysis showed that DOC has the highest correlation with TOC. Based on the results of the correlation analysis, the single- and multiple-regression models were developed using Biochemical Oxygen Demand(BOD), Chemical Oxygen Demand(COD), and TOC. The results of the prediction accuracy for three different regression models showed that the single-regression model with TOC was better than those of the other multiple-regression models. The trend analysis using extended average concentration DOC data shows that DOC tends to decrease reflecting watershed management. This study could contribute to assessment and management of organic water pollution in mixed land-use watershed by suggesting methods for assessment of unmeasured DOC concentration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8222-8227
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• 2015

The visibility range is defined from where one can see, which can be changed by the character sizes and illuminances and so on, which of one-hundred and twelve students are measured for three illuminances and three character sizes in this paper. In determining the character sizes and illuminances, the visibility range can be an important data. Functions are proposed whose independent variable is illuminance and whose dependent variable is visibility range in order to predict the visibility range of unmeasured illuminances. The fractional functions are used for three character sizes because the visibility range is invariant according to illuminance. There are three parameters to be determined - k, m, n, which are selected based on the measured visibility ranges. Because the visibility ranges of three character sizes are measured, three k's can be calculated. In this paper the case of minimum variance of three k's is selected, and three parameters - k,m,n- of that case is selected. The three functions according to three character sizes are proposed. The small differences between the measured data and the postulated functions verifies the accuracy of the functions.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.35-43
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• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.

• Journal of Korean Society of Disaster and Security
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• v.17 no.1
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• pp.1-8
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• 2024

In Korea, mountainous areas cover 60% of the land, leading to increased factors such as concentrated heavy rainfall and typhoons, which can result in debris flow and landslide. Despite the high risk of disasters like landslides and debris flow, there has been a tendency in most regions to focus more on post-damage recovery rather than preventing damage. Therefore, in this study, precise topographic data was constructed by conducting on-site surveys and drone measurements in areas where debris flow actually occurred, to analyze the risk zones for such events. The numerical analysis program RAMMS model was utilized to perform debris flow analysis on the areas prone to debris flow, and the actual distribution of debris flow was compared and analyzed to evaluate the applicability of the model. As a result, the debris flow generation area calculated by the RAMMS model was found to be 18% larger than the actual area, and the travel distance was estimated to be 10% smaller. However, the simulated shape of debris flow generation and the path of movement calculated by the model closely resembled the actual data. In the future, we aim to conduct additional research, including model verification suitable for domestic conditions and the selection of areas for damage prediction through debris flow analysis in unmeasured watersheds.