• Journal of The Korean Association For Science Education
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• v.31 no.1
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• pp.115-127
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• 2011

Analysis frame for undergraduate physics laboratory reports in collecting, processing, and analyzing data was developed. Using the frame and questionaries, we analyzed what difficulties students have in the concepts of error and uncertainty in writing laboratory reports. Students considered repetitive measurement for collecting data, but they didn't express it distinctly in their reports. They also had difficulties in measuring data around the extreme value or the large slope. Especially, most students have had difficulties with error and uncertainty. They can't apply the basic formulation to propagation of error and uncertainty. They also had the difficulties in analyzing data with concepts of error and uncertainty. While most students responded that error and uncertainty is important, there were few students who analyzed the influence of the cause of error on the results quantitatively. The result of the study showed that students have difficulties in writing the laboratory reports because they didn't have the correct concept of the error and uncertainty. So, it is needed to not only teach the physics concept about experiment but to teach basic concept of data collecting, processing, and analyzing specially about error and uncertainty for students as well.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.307-326
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• 2021

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.234-243
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• 2022

In Korea, hail damage occurs every year, and in the case of agriculture, it causes severe field crop and cultivation facility losses. Therefore, it is necessary to develop a hail information service system customized for Korea's primary production and crop-growing areas to minimize hail damage. However, the observation of hail is relatively more difficult than that of other meteorological variables, and the available data are also spatially and temporally variable. A hail information service system was developed to understand the temporal and spatial distribution of hail occurrence. As part of this, a hail observation database was established that integrated the observation data from Korea Meteorological Administration with the information from newspaper reports. Furthermore, a hail risk map was produced based on this database. The risk map presented the nationwide distribution and characteristics of hail showers from 1970 to 2018, and the northeastern region of South Korea was found to be relatively dangerous. Overall, hail occurred nationwide, especially in the northeast and some inland areas (Gangwon, Gyeongbuk, and Chungbuk province) and in winter, mainly on the north coast and some inland areas as graupel (small and soft hail). Analyzing the time of day, frequency, and hailstone size of hail shower occurrences by region revealed that the incidence of large hail stones (e.g., 10 cm at Damyang-gun) has increased in recent years and that showers occurred mainly in the afternoon when the updraft was well formed. By integrating multidisciplinary data, the temporal and spatial gap in hail data could be supplemented. The hail risk map produced in this study will be helpful for the selection of suitable crops and growth management strategies under the changing climate conditions.

• Journal of the Korean earth science society
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• v.37 no.1
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• pp.29-39
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• 2016

In this study, we estimated solar radiation by multiple regression analysis using sunshine duration and precipitation data, which are highly correlated to solar radiation. We found the regression equation using data obtained from GROM (Gangwon Regional Office of Metrology, station 105, 1980-2007) located in Gangneung, South Korea and validated the equation by applying data obtained from new GROM (newly relocated, station 104, 2009-2014) and data obtained from GWNU (Gangneung-Wonju National University, 2013-2014) located between stations 104 and 105. By using sunshine duration data alone, the estimation using data from station 104 resulted in a correlation coefficient of 0.96 and a standard error of $1.16MJm^{-2}$, which was similar to the previous results; the estimation using data from GWNU yielded better results with a correlation coefficient of 0.99 and a standard error of $0.57MJm^{-2}$. By using sunshine duration and precipitation data, the estimation (using data from station 104) yielded a correlation coefficient of 0.96 and a standard error of $0.99MJm^{-2}$, resulting in a lower standard error compared to what was obtained using sunshine duration data alone. The maximum solar radiation bias increased from -26.6% (March 2013) to -31.0% (February 2011) when both sunshine duration and precipitation data were incorporated into the estimation rather than when sunshine duration data alone was incorporated. This was attributed to the concentrated precipitation found during May and July-September, which resulted in negative coefficients of the estimating equation in other months. Therefore, the monthly average solar radiation should be estimated carefully when employing the monthly average precipitation for those places where precipitation is concentrated during summer, such as the Korean peninsula.