• Journal of Climate Change Research
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• v.8 no.2
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• pp.125-143
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• 2017

This study is conducted to estimate potential evapotranspiration of 10 weather observing systems in Andong Dam watershed with FAO56 Penman-Monteith (FAO56 PM) methodology using the meteorological data from 2013 to 2014. Also, assuming that there is no solar radiation data, humidity data or wind speed data, the potential evapotranspiration was estimated by FAO56 PM and the results were evaluated to discuss whether the methodology is applicable when meteorological dataset is not available. Then, the potential evapotranspiration was estimated with Hargreaves method and compared with the potential evapotranspiration estimated by FAO56 PM only with the temperature dataset. As to compare the potential evapotranspiration estimated from the complete meteorological dataset and that estimated from limited dataset, statistical analysis was performed using the Root Mean Square Error (RMSE), the Mean Bias Error (MBE), the Mean Absolute Error (MAE) and the coefficient of determination ($R^2$). Also the Inverse Distance Weighted (IDW) method was performed to conduct spatial analysis. From the result, even when the meteorological data is limited, FAO56 PM showed relatively high accuracy in calculating potential evapotranspiration by estimating the meteorological data.

• Atmosphere
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• v.29 no.3
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• pp.269-282
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• 2019

While it is important to obtain the accurate information on snowfall data due to the increase in damage caused by the heavy snowfall in the winter season, it is not easy to observe the snowfall quantitatively. Recently, snow measurements using a weighing precipitation gauge have been carried out, but there is a problem that high snowfall intensity results in low accuracy. Also, the observed snowfall data are sensitive depending on wind speed, temperature, and humidity. In this study, a new process of quality control for snow water equivalent (SWE) data of the weighing precipitation gauge were proposed to cover the low accuracy of snow data and maximize the data utilization. Snowfall data (SWE) observed by Pluvio, Parsivel, snow-depth meter using laser or ultrasonic, and rainfall gauge in Cloud Physics Observation Site (CPOS) were compared and analyzed. Applying the QC algorithm including the use of number of hydrometeor particles as reference, the increased SWE per the unit time was determined and the data noise was removed and marked by flag. The SWE data converted by the number concentration of hydrometeor particles are tested as a method to restore the QC-removed data, and show good agreement with those of the weighing precipitation gauge, though requiring more case studies. The three events data for heavy snowfall disaster in Pyeongchang area was analyzed. The SWE data with improved quality was showed a good correlation with the eye-measured data ($R^2$ > 0.73).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.341-343
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• 2019

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

• Journal of the Korea Institute of Building Construction
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• v.21 no.1
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• pp.41-49
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• 2021

This study was to solve the lift problem of the existing unit curtain wall type by using the vertical material mullion as a rail in curtain wall, which is recently used as an external finishing material for high-rise buildings. It has been shown that the application of the curtain wall mullion's rail can be quantified even at 20m/sec wind speed through the Mock-Up test. Based on the sites selected for comparison of construction methods, it was analyzed that the construction period could be shortened by 48 days, or about 20 percent. It was analyzed that the number of construction workers could be reduced by about 33 percent from the previous nine to six. Based on these results, assuming the installation of curtain wall units of 10,000㎡, it is judged that construction cost can be reduced by 80% or more.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.4
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• pp.61-75
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• 2021

Roadside greenery in the city is not only a means of reducing fine dust, but also an indispensable element of the city in various aspects such as improvement of urban thermal environment, noise reduction, ecosystem connectivity, and aesthetics. However, in studies dealing with the effect of reducing fine dust through trees in existing urban spaces, microscopic aspects such as the adsorption effect of plants were dealt with, structural changes such as the width of urban buildings and streets, and the presence or absence of trees, Impact studies that reflect the actual form of In this study, the effect of greenery composition applicable to urban space on PM2.5 was simulated through the microclimate epidemiologic model ENVI-met, and field measurements were performed in parallel to verify the results. In addition, by analyzing the results of fine dust background concentration, wind speed, and leaf area index, the sensitivity to major influencing variables was tested. As a result of the study, it was confirmed that the fine dust reduction effect was the highest in the case with a high planting amount, and the reduction effect was the greatest at a low background concentration. Based on this, the cost of planting street green areas and the effect of reducing PM2.5 were compared. The results of this study can contribute as a basis for considering the effect of pedestrian space on air quality when planning and designing street green spaces.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.397-402
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• 2013

In super-tall building construction projects, schedule risk factors which vertically change and are not found in the low and middle-rise building construction influence duration of a project by vertical attribute; and it makes hard to estimate activity or overall duration of a construction project. However, the existing duration estimating methods, that are based on quantity and productivity assuming activities of the same work item have the same risk and duration regardless of operation space, are not able to consider the schedule risk factors which change by the altitude of operation space. Therefore, in order to advance accuracy of duration estimation of super-tall building projects, the degree of changes of these risk factors according to altitude should be analyzed and incorporated into a duration estimating method. This research proposes a simulation model using Monte Carlo method for estimating activity duration incorporating schedule risk factors by weather conditions in a super-tall building. The research process is as follows. Firstly, the schedule risk factors in super-tall building are identified through literature and expert reviews, and occurrence of non-working days at high altitude by weather condition is identified as one of the critical schedule risk factors. Secondly, a calculating method of the vertical distributions of the weather factors such as temperature and wind speed is analyzed through literature reviews. Then, a probability distribution of the weather factors is developed using the weather database of the past decade. Thirdly, a simulation model and algorithms for estimating non-working days and duration of each activity is developed using Monte-Carlo method. Finally, sensitivity analysis and a case study are carried out for the validation of the proposed model.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.795-805
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• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2461-2476
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• 2013

To assess environmental contamination with carcinogens, carbonaceous compounds, water-soluble ionic species and trace gaseous species were identified and quantified every three hours for three days st three different atmospheric layer at the heart of chiang-Mai, bangkok and hat-Yai from December 2006 to February 2007. A DRI model 2001 Themal/Optical Carbon Analyzer with the IMPROVE thermal/optical reflectance (TOR) protocol was used to quantify the organic carbon(OC) and elemental carbon content in $PM_{10}$. Diurnal and vertical variability was also carefully investigated. In general, OC and EC contenttration shoeed the highest values at the monitoring period o 21.00-00.00 as consequences of human activities at night bazaar coupled with reduction of mixing layer, decreased wind speed and termination of photolysis nighttime. Morning peaks of carboaceous compounds were observed during the sampling period of 06:00 -09:00, emphasizing the main contribution of traffic emission in the three cities. The estimation of incremental lifetime partculate matter exposure (ILPE) raises concern of high risk of carbonaceous accumulation over workers and residents living close to the observatory sites. The average values of incremental lifrtime particulate matter exposure (ILPE) of total carbon at Baiyoke Suit Hotel and Baiyoke Sky Hotel are approsimately ten time shigher then those air sample collected at prince of songkla University Hat-Yai campus corpse incinerator and fish-can maufacturing factory but only slightly higher than those of rice straw burnig in Songkla province. This indicates a high risk of developing lung cancer and other respiratory diseases across workers and residents living in high buildings located in Pratunam area. Using knowledge of carbonaceous fractions in $PM_{10}$, one can estimate the gas-particle partitioning of polycyclic aromatic hydrocarbons (PAHs). Dachs-Eisenreich model highlights the crucial role of adsorption in gas-particle partitioning of low molecular weight PAHs, whereas both absorption and adsorption tend to account for gas-particle partitioning of high molecular weight PAHs in urban residential zones of Thailand. Interestingly, the absorption mode alone plays a minor role in gas-partcle partitiining of PAHs in Chiang-Mai, Bangkok and hat-Yai.

• Journal of Environmental Science International
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• v.26 no.2
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• pp.183-200
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• 2017

The classification of airflow patterns during high ozone ($O_3$) and $PM_{10}$ episodes on Jeju Island in recent years (2009-2015), as well as their correlation with meteorological conditions according to classified airflow patterns were investigated in this study. The airflow patterns for $O_3$ and $PM_{10}$ were classified into four types (Types A-D) and three types (Types E-G), respectively, using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and synoptic weather charts. Type A was the most dominant airflow pattern for $O_3$ episodes, being characterized by the transport of airflows from urban and industrial areas in China with the highest frequency (about 69%, with a mean of 67 ppb). With regard to the $PM_{10}$ episodes, Type E was the most dominant airflow pattern, and was mostly associated with long distance transport from Asian dust source regions along northwesterly winds, having the highest frequency (about 92%, with a mean of $136{\mu}g/m^3$). The variations in the concentration of $O_3$ and $PM_{10}$ during the study period were clarified in correlation with two pollutant and meteorological variables; for example, the high (low) $O_3$ and $PM_{10}$ concentrations with high (low) air temperature and/or wind speed and vice versa for precipitation. The contribution of long-range transport to the observed $PM_{10}$ levels in urban sites for different airflow patterns (Types E-F), if estimated in comparison to the data from the Gosan background site, was found to account for approximately 87-93% (on average) of its input. The overall results of the present study suggest that the variations in $O_3$ and $PM_{10}$ concentrations on Jeju Island are mainly influenced by the transport effect, as well as the contribution of local emissions.