• Wind and Structures
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• 제1권2호
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• pp.145-160
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• 1998

In recent years, the effects of a possible climate change have been discussed in regard to wind loading on buildings and structures. Simple scenarios based on the assumption of global warming suggest an increase of storm intensities and storm frequencies and a possible re-distribution of storm tracks. Among recent publications, some papers seem to verify these scenarios while others deny the influence of climatic change. In an introductory step, the paper tries to re-examine these statements. Based on meteorological observations of a weather station in Germany, the existence of long-term trends and their statistical significance is investigated. The analysis itself is based on a refined model for the wind climate introducing a number of new basic variables. Thus, the numerical values of the design wind loads used in modern codes become more justified from the probabilistic point of view.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 한국농림기상학회 2013년도 추계 학술발표논문집
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• pp.102-105
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• 2013

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2016년도 학술발표회
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• pp.9-9
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• 2016

많은 연구들에서 단변량 수문 변량들에 대한 불확실성 분석이 이루어지고 있지만, 다변량에 대한 불확실성에 관한 연구는 아직까지 정확하게 이루어지고 있지 않은 실정이다. 이에 본 연구에서는 갈수기(12월~4월)의 강수, 온도와 남방진동(El Ni?o-Southern Oscillation, ENSO)과 같은 수문기상학적 변량들 사이의 시간에 따른 변동 구조를 조사하고, 식별된 패턴을 이용한 강우와 온도의 예측 향상 가능성을 살펴보았다. 수문기상학적 변수간의 시변성 구조를 이해하기 위해서 각각의 단변량 매개변수와 시간에 따라 변화하는 Copula 매개변수를 동시에 추정할 수 있는 Copula 함수 기반의 새로운 다변량 비정상성 모델을 개발하고자 한다. 강우와 온도의 비정상정 단변량 분포를 생성하기 위해 ENSO 지표 또는 시계열 예측인자와 함께 시변성 모델을 적용할 수 있다. 최종적으로, 확인된 시간 변동적인 구조와 연관된 종관 패턴을 나타내고 논의하고자 한다.

• Journal of Korea Multimedia Society
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• 제25권2호
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• pp.287-296
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• 2022

Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.

• Journal of Korea Artificial Intelligence Association
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• 제1권2호
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• pp.15-20
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• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2009년도 춘계학술대회 논문집
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• pp.561-564
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• 2009

In order to reduce damage from natural disasters, prevention activities through analysis and predicting based on meteorological factor and damage data is required. Other countries already have continuously studied on natural disasters and developed reducing disasters damage. But the risk assessment model for natural disaster is not to Korea. Therefore, a previous model of hurricane, Florida Public Hurricane Loss Model(FPHLM), is the basis and is applying to domestic situation. Accordingly, this study introduces the variables selecting process because input variables should be selected under Korea present state and be used. The estimating representative damage method would be necessary along with selecting housing types representing relevant areas because estimating damage amount of all over relevant areas housing was very hard during damage estimating process. But there is no exact representative housing types in the Korea. Therefore, we select housing types applicable to risk assessment model for natural disasters representing the Korea through previous studies and literature reviews. We using ASCE 7-98(Minimum Design Loads for Buildings and Other Structures, 1998) standard which estimated wind load using 3-second gust. ASCE 7-98 divided Main Wind Force Resistance System(MWFRS) and Component and Cladding(C&C) and it estimated wind load. Therefore, we estimate wind load affected by 3-second gust of a typhoon Maemi through calculating wind load process using selected representative detached house types in the process of selecting input variables for previous disaster predict model. The result of houses damage amount is about 230 hundred million won. This values are limit the 1-story detached dwelling, 19~29pyeong(62.81~95.56 $m^2$) of total area and flat roof. Therefore, this process is possible application to other type houses.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 한국환경과학회 1997년도 가을 학술발표회 프로그램
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• pp.1.1-32
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• 1997

Atmospheric monitoring capabilities were established in 1988 by the University of Florida at Duke forest, near Durham. NC: Cary forest, near Gainesville, FL: and Austin forest, near Nacogdoches, TX. Continuous (hourly averaged) measurements of air quality (ozone, nitrogen oxides and sulfur dioxide) and meteorological variables were made at these three low elevation (< 200 meters), rural locations in the southeastern U.S. for more than three years. During the same period at these sites wet and dry acid deposition samples were collected and analyzed on an event and weekly basis, respectively The monitoring locations were selected to determine actual atmospheric exposure indices for southern pine species in support of on-site surrogate exposure chamber studies conducted by Southern Commercial Forest Research Cooperative (SCFRC) investigators. Daily and quarterly averaged ozone maxima were higher (55 ppb) at the northernmost site in the network (Duke forest) in the second and third quarters (spring and summer seasons) and lower (35 ppb) in the first and fourth quarters (winter and fall seasons), when compared to ozone levels at the two southernmost sites (Cary and Austin forests). Seasonal ozone levels at the latter two sites were similar Nitrogen oxieds and sulfur dioxide levels were insignificant (< 5 ppb) most of the time at all sites, although soil emissions of NO at two sites were found to influence nighttime ozone concentrations. Typical maximum quarterly and annual aggregate ozone exposure indices were significantly higher at Duke forest (92.5/259 ppm-hr) than those values observed at the two southern sites (65.6/210 ppm-hr). Acid deposition (wet and dry) components concentrations and deposition fluxes observed at the Duke forest, NC piedmont site, were generally greater, dependent on site and season, than corresponding variables measured at either of the two southern coastal plain sites (Cary and Austin forests). Acid deposition variables observed at the latter two sites were remarkably similar, both qualitatively and quantitatively, although the sites were located 1300 km apart. A comparison of deposition fluxes of elemental nitrogen (NO3, NH4') and sulfur (5042-, SO3) components in wet and dry forms indicated that wet deposition accounts for approximately 70% of the total nitrogen and 73% of the total sulfur input on an annual equivalent basis at all sites.

• Journal of Korean Society on Water Environment
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• 제39권1호
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Atmosphere
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• 제25권1호
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• Journal of the Korean Data and Information Science Society
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• 제28권5호
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• pp.1125-1132
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• 2017

In a situation where the severity and frequency of drought events getting stronger and higher, many studies related to drought forecast have been conducted to improve the drought forecast accuracy. However it is difficult to predict drought events using a single model because of nonlinear and complicated characteristics of temporal behavior of drought events. In this study, in order to overcome the shortcomings of the single model approach, we first build various single models capable to explain the relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and other independent variables such as world climate indices. Then, we developed a combined models using Stochastic Gradient Descent method among Ensemble Learnings.