• International conference on construction engineering and project management
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• 2015.10a
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• pp.411-414
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• 2015

The purpose of this study is to conduct a comparative analysis between Korea and the United States of the supply process of unit modular housing at both the factory production phase and the transportation and lifting phase, as part of an attempt to invigorate the unit modular housing market in Korea. Unlike the practice in the United States, one of Korea's unique characteristics is that the floor is constructed with reinforced concrete and hot water heating system. To do this, the wet method in Korea is used that includes concrete placement, curing and constructing hot water floor heating system at the factory production phase, which results in a longer production time and also requires the lifting of heavier loads. In the United States, interior and exterior finishing works of modular housing are performed by different companies, and the distance between the unit module factory and the construction site is quite far. This kind of dualized production structure may cause confusion when it comes to schedule management, procurement management, and stock management. Moreover, problems caused by external environmental factors such as wind and rainfall were reported in the course of long-distance transportation. The results of this case comparison are expected to provide fundamental data that will reduce the amount of trial and error in the unit module production, transportation and lifting work in Korea, which has a comparatively small number of unit modular housing cases.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.367-385
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• 2022

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1409-1416
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• 2015

The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.28-38
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• 2004

The purpose of this study is to analyze the urban hydrologic state by the use of GIS, resolution and interpolation. The determination coefficient($R^2$) and Regression Formula were derived from the contour of digital map for the accuracy, and DEM data was made by using TIN interpolation by the size of the grid. By using the observed DEM data, topographical factors were extracted from the small basin, size, the width of a basin and the slope, and were applied in the urban runoff model. Through the model, we tried to find out the most suitable runoff model in a small basin of Yosu-Munsu area. As a result of applying models to the drainage considered, the runoff hydrograph estimated by SWMM model was closer to the observed one than that estimated by ILLUDAS model. The difference between the runoff hydrograph by SWMM and the observed one is maximum error of 19%, minimum error of 5% and average error of 13%. The influence of duration in contrast to pick time is insignificant in a urban small basin. As a conclusion of this study, SWMM model was more suitable and applicable for the urban runoff model than ILLUDAS model due to its accuracy and various abilities.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.2
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• pp.72-78
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• 2009

The demand for rainfall data in gridded digital formats has increased in recent years due to the close linkage between hydrological models and decision support systems using the geographic information system. One of the most widely used tools for digital rainfall mapping is the PRISM (parameter-elevation regressions on independent slopes model) which uses point data (rain gauge stations), a digital elevation model (DEM), and other spatial datasets to generate repeatable estimates of monthly and annual precipitation. In the PRISM, rain gauge stations are assigned with weights that account for other climatically important factors besides elevation, and aspects and the topographic exposure are simulated by dividing the terrain into topographic facets. The size of facet or grid cell resolution is determined by the density of rain gauge stations and a $5{\times}5km$ grid cell is considered as the lowest limit under the situation in Korea. The PRISM algorithms using a 270m DEM for South Korea were implemented in a script language environment (Python) and relevant weights for each 270m grid cell were derived from the monthly data from 432 official rain gauge stations. Weighted monthly precipitation data from at least 5 nearby stations for each grid cell were regressed to the elevation and the selected linear regression equations with the 270m DEM were used to generate a digital precipitation map of South Korea at 270m resolution. Among 1.25 million grid cells, precipitation estimates at 166 cells, where the measurements were made by the Korea Water Corporation rain gauge network, were extracted and the monthly estimation errors were evaluated. An average of 10% reduction in the root mean square error (RMSE) was found for any months with more than 100mm monthly precipitation compared to the RMSE associated with the original 5km PRISM estimates. This modified PRISM may be used for rainfall mapping in rainy season (May to September) at much higher spatial resolution than the original PRISM without losing the data accuracy.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.3
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• pp.178-184
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• 2014

In this study, monitoring method which is more economic and easier in maintenance comparing to existing monitoring system was scrutinized for application to sewer intercepting chamber of 10.2 ha basin area by estimating CSOs (Combined Sewer Overflows) quantity and quality with 2 rainfall events using electrical conductivity data and civil research model. The result showed that determination coefficient of flow estimation by EC (Electrical Conductivity) dilution ratio and observed data was over 0.86 for all cases and the accuracy of estimation was improved from 0.5 to 0.8 for determination coefficient ($R^2$) and from 54.1% to 68.5% for accumulation frequency of relative error by considering antecedent dry days and rainfall duration. CSOs water quality estimation results by civil research model showed that determination coefficients were 0.64~0.97 for BOD and 0.70~0.95 for SS.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.117-125
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• 2013

Flooding of riparian groundwater caused by changes in rainfall patterns has become a critical problem in areas of agricultural and arable land. Therefore, quantitative analysis of direct runoff and baseflow, which are the most important factors in determining the flow rate of a river, is required to clarify the flooding mechanisms of riparian groundwater. In this study, baseflow obtained using the WHAT system of hydrograph analysis based on Web GIS, and baseflow measured from direct runoff were quantitatively analyzed. Baseflow during the rainy season was 0.489 $m^3/s$ on 17 July 2012, 0.260 $m^3/s$ on 18 July 2012, and 0.279 $m^3/s$ on 19 July 2012, while that during the dry season was 0.006 $m^3/s$ on 6 March 2013 and 0.009 $m^3/s$ on 30 March 2013. The results show that an increase in baseflow occurred during the rainy season in the alluvial area of a riparian zone, and that the measurement error was less during the dry season than during the rainy season.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.647-659
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• 2017

Garlic and onion are grown in major cultivation regions that depend on the crop condition and the meteorology of the production area. Therefore, when yields are to be predicted, it is reasonable to use a statistical model in which both the crop and the meteorological elements are considered. In this paper, using a multiple linear regression model, we predicted garlic and onion yields in major cultivation regions. We used the MODIS NDVI that reflects the crop conditions, and six meteorological elements for 7 major cultivation regions from 2006 to 2015. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, the MODIS NDVI in February was chosen the significant independent variable of the garlic and onion yield prediction model. In the case of meteorological elements, the garlic yield prediction model were the mean temperature (March), the rainfall (November, March), the relative humidity (April), and the duration time of sunshine (April, May). Also, the rainfall (November), the duration time of sunshine (January), the relative humidity (April), and the minimum temperature (June) were chosen among the variables as the significant meteorological elements of the onion yield prediction model. MODIS NDVI and meteorological elements in the model explain 84.4%, 75.9% of the garlic and onion with a root mean square error (RMSE) of 42.57 kg/10a, 340.29 kg/10a. These lead to the result that the characteristics of variations in garlic and onion growth according to MODIS NDVI and other meteorological elements were well reflected in the model.