• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.41-41
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• 2022

홍수는 단기간의 사상이다. 평상시 유량은 일단위로 연속유량이라 한다. 홍수해석은 사상 모형을 이용하고, 물 이용의 용수계획에서는 연속 모형에 의한다. 평상시 유량을 홍수처럼 10분, 30분, 60분 단위로 해석할 수 있으면 여러 가지로 편리하다. 홍수기에는 홍수와 이수를 동시에 분석할 수 있는 이점이 있다. 평상시에는 수문자료가 생성되는 기본단위가 10분, 60분이기 때문에 이와 일치하여 유량을 해석할 수 있는 이점이 있다. 특히 저수지에서는 운영자료가 저수율 자료만 관리되고 있는 현실을 감안하면, 연속 홍수모의의 필요성은 매우 높다. 다목적댐도 그 편의성은 말로 형용할 수 없는 수준이다. 홍수모의는 첨두유량도 중요하고, 전체 누적유량도 중요하다. 여기서는 당초 일 단위로 개발된 ONE 모형으로 연속 홍수모의의 가능성을 타진했다. 모형의 검증은 홍수사상 마다 훨씬 긴 장기간의 댐의 유입량, 저수량 오차로 실시했다. 유입량이 누적되면 저수량이 되기 때문에 저수량을 비교하면 확실한 검증 방법이 된다. 유역면적 930.0km², 총저수량 8억1,500만m³인 용담댐과 유역면적 218.80km², 유효저수량 3,498만m³인 탑정지를 대상으로 60단위의 장기간 연속 홍수모의 결과를 제시한다. 첫째, 용담댐에 대해 2020년 3월1일부터 6월30일까지 연속유입량을 모의한 결과(ONE모형 매개변수 α=3.18), 면적우량은 최대 12.5mm, 총 371.2mm(3억4,522만m³)였고, 유입량은 최대 1,363.0m³/s, 총 1억8,326만m³로 유출률 53.1%였다, 관측 유입량은 최대 766.1m³/s, 총 2억9,152만m³로 유출률 84.4%로 나타났다. 관측 유입량이 높은 것으로 평가했는데 그 이유는 산정된 유입량이 넓은 수면적에서 오는 음유입량이 발생하는데 이를 0으로 처리하고, 음의 누적 값이 전체유량에 더해지는 계산의 한계에서 비롯한다. 이는 현실적 제한이며, 개선이 필요하다. 댐 수위로 검증한 결과는 관측수위는 EL.257.97~262.92m, 평균 EL.260.40m, 모의수위는 EL.257.22~262.88m, 평균 EL.260.02m로 나타났고, RMSE는 0.174, NSE는 0.959, R²는 0.968로 만족한 결과를 얻었다. 둘째, 탑정지에 대해 2020년 3월1일부터 6월30일까지 연속유입량을 모의한 결과(ONE모형 매개변수 α=3.18), 면적우량은 최대 18.5mm, 총 311.4mm(6,813만m³)였고, 유입량은 최대 187.8m³/s, 총 3,691만m³로 유출률 54.2%였다. 저수지 수위로 검증한 결과 관측수위는 EL.26.55~29.79m, 평균 EL.29.01m, 모의수위는 EL.26.16~29.92m, 평균 EL.29.07m로 나타났고, RMSE는 0.563, NSE는 0.877, R²는 0.943로 만족한 결과를 얻었다. 정리하면 2020년 4개월의 장기간 용담댐과 탑정지에 대한 1시간 간격의 연속 홍수모의의 결과는 그 활용 가능성이 충분하다고 말하고 있다. 이 결과로부터 평상시 댐과 저수지의 실시간 운영자료 검증 및 생산체제의 수문관측업무에 활용 가능한 것으로 평가했다.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.434-440
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• 2022

The ICAO (International civil aviation organization)recommended the implementation of the GANP (global air navigation plan) for strategic decision-making and air traffic management evaluation. In this study, we proposed a new method for finding the route distance from KPI (key performance indicator) 05 actual route extension presented for air traffic management evaluation. For this purpose, we collected trajectory data for one month and calculated the en-route distances using the methods presented in ICAO and the methods presented by this author. In the ICAO method, the intersection point must be estimated through the equation of a circle for radius 40 NM and the equation of a straight line for an inner and outer point close to a circle in the track data, and four flight distances are calculated to calculate the en-route distance. In the method presented in this study, two flight distances are calculated without estimating the intersection point to calculate the en-route distance. To determine the error between the two methods, we used the performance evaluation index RMSE (root mean square error) and the determination factor R² of the regression model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.775-784
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• 2023

In terms of flood management, it is necessary to analyze quantitative rainfall and runoff from a spatial and temporal perspective and to analyze runoff for heavy rainfall events that are concentrated within a short period of time. The simulation and analysis results of rainfall-runoff models vary depending on the type and input data. In particular, rainfall data is an important factor, so calculating areal mean rainfall is very important. In this study, the areal mean rainfall of the Samcheok Osipcheon(Riv.) watersheds located in the mountainous terrain was calculated using the Arithmetic Mean Method, Thiessen's Weighting Method, and the Isohyetal Method, and the rainfall-runoff results were compared by applying the distributional model S-RAT and the lumped model HEC-HMS. The results of the temporal transferability study showed that the combination of the distributional model and the Isohyetal Method had the best statistical performance with MAE of 64.62 m³/s, RMSE of 82.47 m³/s, and R² and NSE of 0.9383 and 0.8547, respectively. It is considered that this study was properly analyzed because the peak flood volume occurrence time of the observed and simulated flows is within 1 hour. Therefore, the results of this study can be used for frequency analysis in the future, which can be used to improve the accuracy of simulating peak flood volume and peak flood occurrence time in mountainous watersheds with steep slopes.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Journal of the Korean Geosynthetics Society
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• v.23 no.3
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• pp.15-22
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• 2024

Predicting ground settlement during the improvement of soft ground and the construction of a structure is an crucial factor. Numerous studies have been conducted, and many prediction equations have been proposed to estimate settlement. Settlement can be calculated using the compression index of clay. In this study, data on water content, void ratio, liquid limit, plastic limit, and compression index from the Busan New Port area were collected to construct a dataset. Correlation analysis was conducted among the collected data. Machine learning algorithms, including Random Forest, Neural Network, Linear Regression, Ada Boost, and Gradient Boosting, were applied using the Orange mining program to propose compression index prediction models. The models' results were evaluated by comparing RMSE and MAPE values, which indicate error rates, and R² values, which signify the models' significance. As a result, water content showed the highest correlation, while the plastic limit showed a somewhat lower correlation than other characteristics. Among the compared models, the AdaBoost model demonstrated the best performance. As a result of comparing each model, the AdaBoost model had the lowest error rate and a large coefficient of determination.

• Atmosphere
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• v.17 no.2
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• pp.125-133
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• 2007

Cloud amount calculation algorithm was developed using MTSAT-1R satellite data. The cloud amount is retrieved at 5 km ${\times}$ 5 km over the Korean Peninsula and adjacent sea area. The algorithm consists of three steps that are cloud detection, cloud type classification, and cloud amount calculation. At the first step, dynamic thresholds method was applied for detecting cloud pixels. For using objective thresholds in the algorithm, sensitivity test was performed for TBB and Albedo variation with temporal and spatial change. Detected cloud cover was classified into 3 cloud types (low-level cloud, cirrus or uncertain cloud, and cumulonimbus type high-level cloud) in second step. Finally, cloud amount was calculated by the integration method of the steradian angle of each cloud pixel over $3^{\circ}$ elevation. Calculated cloud amount was compared with measured cloud amount with eye at surface observatory for the validation. Bias, RMSE, and correlation coefficient were 0.4, 1.8, and 0.8, respectively. Validation results indicated that calculated cloud amount was a little higher than measured cloud amount but correlation was considerably high. Since calculated cloud amount has 5km ${\times}$ 5km resolution over Korean Peninsula and adjacent sea area, the satellite-driven cloud amount could show the possibility which overcomes the temporal and spatial limitation of measured cloud amount with eye at surface observatory.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.6
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• pp.441-448
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• 2018

Recently, measurements on clutter loss due to buildings in urban/suburban areas at 3, 6, 10, 18, and 24 GHz have been performed by the Radio Research Agency with the purpose of predicting the clutter loss close to actual urban/suburban propagation for 5G mobile communication. In this work, we have compared the urban clutter loss to suburban clutter loss for a transmit antenna height of 85 m. Furthermore, we have estimated the error between the predicted loss as per ITU-R P.2108 and the measured clutter loss. Our results indicate that for higher frequencies, the measured clutter loss in urban/suburban areas is higher and so lower than the predicted clutter loss. In conclusion, it is necessary to improve the prediction model for clutter loss by taking into account the measured clutter loss in urban/suburban areas in the prediction model.

• Proceedings of the KSRS Conference
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• v.1
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• pp.278-281
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• 2006

Korea Meteorological Administration(KMA) has issued the tropical storm(typhoon) warning or advisories when it was developed to tropical storm from tropical depression and a typhoon is expected to influence the Korean peninsula and adjacent seas. Typhoon information includes current typhoon position and intensity. KMA has used the Dvorak Technique to analyze the center of typhoon and it's intensity by using available geostationary satellites' images such as GMS, GOES-9 and MTSAT-1R since 2001. The Dvorak technique is so subjective that the analysis results could be variable according to analysts. To reduce the subjective errors, QuikSCAT seawind data have been used with various analysis data including sea surface temperature from geostationary meteorological satellites, polar orbit satellites, and other observation data. On the other hand, there is an advantage of using the Subjective Dvorak Technique(SDT). SDT can get information about intensity and center of typhoon by using only infrared images of geostationary meteorology satellites. However, there has been a limitation to use the SDT on operational purpose because of lack of observation and information from polar orbit satellites such as SSM/I. Therefore, KMA has established Advanced Objective Dvorak Technique(AODT) system developed by UW/CIMSS(University of Wisconsin-Madison/Cooperative Institude for Meteorological Satellite Studies) to improve current typhoon analysis technique, and the performance has been tested since 2005. We have developed statistical relationships to correct AODT CI numbers according to the SDT CI numbers that have been presumed as truths of typhoons occurred in northwestern pacific ocean by using linear, nonlinear regressions, and neural network principal component analysis. In conclusion, the neural network nonlinear principal component analysis has fitted best to the SDT, and shown Root Mean Square Error(RMSE) 0.42 and coefficient of determination($R^2$) 0.91 by using MTSAT-1R satellite images of 2005. KMA has operated typhoon intensity analysis using SDT and AODT since 2006 and keep trying to correct CI numbers.

• Advances in Computational Design
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• v.2 no.3
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.94-94
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• 2011

최근에 산악지역에서의 국지성 강우에 의한 사고 발생이 증가하고 있고, 2009년에는 북한의 무단 댐방류로 인해 인명피해가 발생함에 따라서 산악이나 북한 지역과 같은 지역의 모니터링이 필요하게 되었으며, 강수량의 기후학적 분포의 특성과 같은 장기적인 강수량 정보가 필요하게 되었다. 레이더는 넓은 영역에 대해서 시 공간적으로 고해상도의 자료를 제공할 수 있기 때문에 국지 규모의 단시간 강수량 정보를 제공하는데 유용하다. 국립기상연구소(National Institute of Meteorological Research; NIMR)는 기존의 층운형 Z-R 관계식(Z=$200R^{1.6}$, Marshall-Palmer, 1948)을 이용한 레이더 강우강도 산출에서 과소추정 문제를 개선하기 위해 레이더-AWS 강우강도(Radar-AWS Rain rate; RAR) 산출 시스템을 개발하여 현재 운영하고 있다. RAR 산출 알고리즘은 각 레이더에 대해서 레이더 강우강도와 지상 AWS 우량계 자료를 비교하여 실시간으로 Z-R 관계식을 산출하여, 레이더 반사도를 강우강도로 변환하고, 이를 합성하여 한반도 영역에 대해서 강우강도 정보를 제공한다. 2010년에는 RAR 자료와 지상 AWS 우량계 자료를 이용하여 레이더-AWS 누적강수량을 산출하는 시스템을 구축하였으며, 현재 시험운영 중에 있다. 본 연구에서는 레이더-AWS 누적강수량의 정확도를 평가하기 위해서 2009년에 대해 레이더-AWS 누적강수량 자료와 지상 AWS 누적강수량 자료에 대해 RMSE, Bias 등의 통계값을 산출하였으며, 북한 지역에 대한 적용가능성을 분석하기 위해서 레이더 관측 반경 내의 북한 지역의 GTS 지점 자료를 이용하여 사례 분석하였다. 본 연구는 레이더 자료를 이용한 지상 관측 공백지역의 강수량에 대한 모니터링을 통하여 이러한 지역의 사고에 대비할 수 있고, 기후학적인 강수량 정보 제공 및 향후 유역별 레이더 면적강수지도 시험판 개발을 통하여 수문 기상 분야에 적용하여 효과적인 물관리에 기여할 수 있을 것으로 사료된다.