• 대기
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• 제24권2호
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• pp.159-171
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• 2014

This study investigated the temporal and spatial characteristics of summertime (June-August) precipitation over Korean peninsula, using Korea Meteorological Administration (KMA)is Automated Synoptic Observing System (ASOS) data for the period of 1973-2010 and Automatic Weather System (AWS) data for the period of 1998-2010.The authors looked through climatological features of the summertime precipitation, then examined the degree of locality of the precipitation, and probable precipitation amount and its return period of 100 years (i.e., an extreme precipitation event). The amount of monthly total precipitation showed increasing trends for all the summer months during the investigated 38-year period. In particular, the increasing trends were more significant for the months of July and August. The increasing trend of July was seen to be more attributable to the increase of precipitation intensity than that of frequency, while the increasing trend of August was seen to be played more importantly by the increase of the precipitation frequency. The e-folding distance, which is calculated using the correlation of the precipitation at the reference station with those at all other stations, revealed that it is August that has the highest locality of hourly precipitation, indicating higher potential of localized heavy rainfall in August compared to other summer months. More localized precipitation was observed over the western parts of the Korean peninsula where terrain is relatively smooth. Using the 38-years long series of maximum daily and hourly precipitation as input for FARD2006 (Frequency Analysis of Rainfall Data Program 2006), it was revealed that precipitation events with either 360 mm $day^{-1}$ or 80 mm $h^{-1}$ can occur with the return period of 100 years over the Korean Peninsula.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• 한국수자원학회논문집
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• 제54권spc1호
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• pp.1071-1081
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• 2021

본 연구에서는 기상청에서 수행하는 기존의 기상 관측에 대한 품질관리 절차 이외에 향후 스마트시티 등에서 활용될 수 있는 머신러닝 기반의 Internet of Things (IoT) 도시기상 관측 자료에 대한 품질검사 기준을 제안한다. 현재 기상청에서 종관기상관측(Automated Synoptic Observing System, ASOS)과 방재기상관측(Automatic Weather System, AWS) 기반으로 설정한 기준이 도시기상에 적합한지 확인하기 위하여 서울시에 설치된 SKT AWS 자료를 기반으로 사용성을 검증하였고, IoT 자체의 데이터가 가지는 특성을 고려하여 최종적으로 머신러닝 기반의 품질검사 알고리즘을 제안하였다. 품질검사 방법으로는 IoT 기기 자체에 대한 결측값 검사, 값 패턴 검사, 충분 데이터 검사, 통계적 범위 이상 검사, 시간값 이상 검사, 공간값 이상 검사를 먼저 수행하고, 기상청에서 제시하고 있는 기상 관측에 대한 품질검사인 물리한계검사, 단계검사, 지속성 검사, 기후범위 검사, 내적 일치성 검사를 5가지 기상요소에 대하여 각각 수행하였다. 제안한 알고리즘의 검증을 위하여 인천광역시 송도에 위치한 관측소에 실제 IoT 도시기상관측 데이터에 이를 적용하였다. 이를 통해 기존의 기상청 QC로는 확인할 수 없었던 IoT 기기가 가질 수 있는 결함을 확인할 수 있고, 알고리즘에 대한 검증을 진행하여 향후 스마트시티에 설치될 IoT 기상관측기기에 대한 품질검사 방법을 제안한다.

• The Journal of the Acoustical Society of Korea
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• 제23권3E호
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• pp.91-97
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• 2004

In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• 대기
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• 제29권2호
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• pp.183-196
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• 2019

High-quality and high-resolution meteorological information is essential to reduce damages due to disastrous weather phenomena such as flash flood, strong wind, and heat/cold waves. There are many meteorological observation stations operated by Korea Meteorological Administration (KMA) in Seoul Metropolitan Area (SMA). Nonetheless, they are still not enough to represent small-scale weather phenomena like convective storm cells due to its poor resolution, especially over urban areas with high-rise buildings and complex land use. In this study, feasibilities to use additional pre-existing networks (e.g., operated by local government and private company) are tested by investigating the effects of network density on the gridded horizontal distribution of two meteorological variables (temperature and precipitation). Two heat wave event days and two precipitation events are chosen, respectively. And the automatic weather station (AWS) networks operated by KMA, local-government, and SKTechX in Incheon area are used. It is found that as network density increases, correlation coefficients between the interpolated values with a horizontal resolution of 350 m and observed data also become large. The range of correlation coefficients with respect to the network density shows large in nighttime rather than in daytime for temperature. While, the range does not depend on the time of day, but on the precipitation type and horizontal distribution of convection cells. This study suggests that temperature and precipitation sensors should be added at points with large horizontal inhomogeneity of land use or topography to represent the horizontal features with a resolution higher than 350 m.

• 대기
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• 제29권4호
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• pp.463-480
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• 2019

This study classified heavy rain types from K-means clustering for the hourly relationship between rainfall intensity and cloud top height over the Korean peninsula, and then examined their statistical characteristics for the period of June~August 2013~2018. Total rainfall amount of warm-type events was 2.65 times larger than that of the cold-type, whereas the lightning frequency divided by total rainfall for the warm-type was only 46% of the cold-type. Typical cold-type cases exhibited high cloud top height around 16 km, large reflectivity in the upper layer, and frequent lightning flashes under convectively unstable condition. Phenomenally, the cold-type cases corresponded to cloud cluster or multi-cell thunderstorms. However, two warm-type cases related to Changma and typhoon were characterized by heavy rainfall due to long duration, relatively low cloud top height and upper-level reflectivity, and the absence of lightning under the convectively neutral and extremely humid conditions. This study further confirmed that the forecast skill of rainfall could be improved by applying correction factor with the overestimation for cold-type and underestimation for warm-type cases in the Local Data Assimilation and Prediction System (LDAPS) operational model (e.g., BIAS score was improved by 5%).

• Advances in Energy Research
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• 제6권2호
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1142-1146
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• 2009

A rainfall simulation and forecasting technique that can generate daily rainfall sequences conditional on multi-model ensemble GCMs is developed and applied to data in Korea for the major rainy season. The GCM forecasts are provided by APEC climate center. A Weather State Based Downscaling Model (WSDM) is used to map teleconnections from ocean-atmosphere data or key state variables from numerical integrations of Ocean-Atmosphere General Circulation Models to simulate daily sequences at multiple rain gauges. The method presented is general and is applied to the wet season which is JJA(June-July-August) data in Korea. The sequences of weather states identified by the EM algorithm are shown to correspond to dominant synoptic-scale features of rainfall generating mechanisms. Application of the methodology to seasonal rainfall forecasts using empirical teleconnections and GCM derived climate forecast are discussed.

• 한국환경과학회지
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• 제5권2호
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• pp.113-130
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• 1996

Diurnal variations of air quality for each season over Taeau city were analyzed using the characteristic features of the various synoptic wind fields. The air quality data which were monitored by four stations are the hourly averaged sulfur dioxide($SO_2$), total suspended particulate(TSP) and oxidants ($O_3$) during the period of 1989 to 1992. The various synoptic wind fields obtained from the 850 hPa geopotential height were divided in to four geostrophic wind directions and two geostrophic wind speeds for each seasons. The synoptic weather conditions were again subdivided info two categories using the lotal cloud amounts, The results shows that diurnal and seasonal variations of the air quality over Taegu city, such as sulfur dioxide, total suspended particulate and oxidants reseal the various characteristics under the same synoptic weather conditions.