• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5015-5022
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• 2010

Most of the existing pattern mining techniques are capable of searching patterns according to the continuous change of the spatial information of an object but there is no constraint on the spatial information that must be included in the extracted pattern. Thus, the existing techniques are not applicable to the optimal path search between specific nodes or path prediction considering the nodes that a moving object is required to round during a unit time. In this paper, the precision of the path search according to the spatial hierarchy is analyzed using the Spatial-Temporal Optimal Moving Pattern(with Frequency & Weight) (STOPM(FW)) algorithm which searches for the optimal moving path by considering the most frequent pattern and other weighted factors such as time and cost. The result of analysis shows that the database retrieval time is minimized through the reduction of retrieval range applying with the spatial constraints. Also, the optimal moving pattern is efficiently obtained by considering whether the moving pattern is included in each hierarchical spatial scope of the spatial hierarchy or not.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.268-268
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• 2021

Groundwater resource is mostly used in Abomey-calavi (southern region of Benin) as main source of water for domestic, industrial, and agricultural activities. Groundwater intake across the region is not perfectly controlled by a network due to the presence of many private boreholes and traditional wells used by the population. After some decades, this important resource is becoming more and more vulnerable and needs more attention. For a better groundwater management in the region of Abomey-calavi, the present study attempts to predict a future probable groundwater drought using Recurrent Neural Network (RNN) for future groundwater level prediction. The RNN model was created in python using jupyter library. Six years monthly groundwater level data was used for the model calibration, two years data for the model test and the model was finaly used to predict two years future groundwater level (years 2020 and 2021). GRI was calculated for 9 wells across the area from 2012 to 2021. The GRI value in dry season (by the end of March) showed groundwater drought for the first time during the study period in 2014 as severe and moderate; from 2015 to 2021 it shows only moderate drought. The rainy season in years 2020 and 2021 is relatively wet and near normal. GRI showed no drought in rainy season during the study period but an important diminution of groundwater level between 2012 and 2021. The Pearson's correlation coefficient calculated between GRI and rainfall from 2005 to 2020 (using only three wells with times series long period data) proved that the groundwater drought mostly observed in dry season is not mainly caused by rainfall scarcity (correlation values between -0.113 and -0.083), but this could be the consequence of an overexploitation of the resource which caused the important spatial and temporal diminution observed from 2012 to 2021.

• The Journal of Bigdata
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• v.6 no.2
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• pp.39-49
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• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• Journal of The Geomorphological Association of Korea
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• v.28 no.3
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• pp.37-49
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• 2021

Analysis and prediction of storm surges are very important because the global warming has raised sea levels and increased the frequency of massive typhoons, accelerating damage of coastal flooding. However, the data for storm surge prediction is lacking due to the short history of observation in South Korea. The purpose of this study is to investigate the spatial and temporal characteristics of the previous surges and tsunamis based on the historical documents published during the Joseon Dynasty. In addition, we tried to evaluate the damage and spatial extent of such disasters, using the expressions about surge records including heights and number of administrative divisions. As a result, a total of 175 records of surges and tsunamis were compiled from 1392 to 1910: 145 events were extracted through the analysis of the ancient documents, and 30 events were from the previous research. Most of the strorm surges occurred along the west coast during summer season. More than half of the total surges were concentrated for 120 years from the mid 1600s to the mid 1700s, which was estimated to be highly relevant to the climate conditions in East Asia during the Little Ice Age. Hazardous areas by storm and tidal surges were also extracted, including Asan, Ganghwa, and Siheung during the Joseon Dyanisity period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.587-599
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• 2023

As climate change increasesthe frequency and risk of flooding in major cities around theworld, the importance ofsimulation technology that can quickly and accurately analyze high-resolution 2D flooding information in large-scale areasis emerging. The physically-based approaches based on the Shallow Water Equations (SWE) often requires huge computer resources hindering high-resolution flood prediction. This study investigated the theoretical background of Weighted Cellular Automata 2D (WCA2D), which simulates spatio-temporal changes offlooding using transition rules and weight-based system, and assessed feasibility to simulate pluvial flooding in the urbancatchment, theOncheon-cheon catchmentinBusan, SouthKorea.Inaddition,the computation performancewas compared by applying versions using OpenComputing Language (OpenCL) andOpenMulti-Processing (OpenMP) parallel computing techniques. Simulationresultsshowed that the maximuminundation depthmap by theWCA2Dmodel cansimilarly reproduce historical inundation maps. Also, it can precisely simulate spatio-temporal changes of flooding extent in the urban catchment with complex topographic characteristics. For computation efficiency, parallel computing schemes, theOpenCLandOpenMP, improved the computation by about 8~14 and 5~6 folds respectively, compared to the sequential computation.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.118.1-118.1
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• 2012

In situ observations from the Wind spacecraft that statistically analyzed the solar wind proton at 1 AU has indicated that the measured proton temperature anisotropies seems to be regulated by the oblique instabilities (the mirror and oblique firehose). This result is in contradiction with the prediction of linear kinetic theory that the ion-cyclotron (for ${\beta}_{\parallel}$ < 2) and parallel firehose (for ${\beta}_{\parallel}$ <10) would dominate over the oblique instabilities. Various kinds of physical mechanisms have been suggested to explain this disagreement between the observations and linear theory. All of the suggestions consider the solar wind as a unoform magnetized plasma. However the real space environment is replete with the intermediate spatio-temporal scale variations associated with various physical quantities, such as the magnetic field intensity and the solar wind density. In this paper we present that the pervasive intermediate-scale temporal variation of the local magnetic field intensity can lead to the modification of the proton temperature anisotropy versus beta inverse correlation for temperature-anisotropy-driven instabilities. By means of quasilinear kinetic theory involving such temporal variation, we construct the simulated solar wind proton data distribution associated the magnetic fluctuations in (${\beta}_{\parallel}$, $T_{\perp}/T_{\parallel}$) space. It is shown that the theoretically simulated proton distribution and a general trend of the enhanced fluctuations bounded by the oblique instabilities are consistent with in situ observations. Furthermore, the measure magnetic compressibility can be accounted for by the magnetic spectral signatures of the unstable modes.

• Atmosphere
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• v.24 no.1
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• pp.1-15
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• 2014

This study compared ensemble mean and probability forecasts of snow depth amount associated with winter storm over South Korea on 28 December 2012 at five operational forecast centers (CMA, ECMWF, NCEP, KMA, and UMKO). And cause of difference in predicted snow depth at each Ensemble Prediction System (EPS) was investigated by using THe Observing system Research and Predictability EXperiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data. This snowfall event occurred due to low pressure passing through South Sea of Korea. Amount of 6 hr accumulated snow depth was more than 10 cm over southern region of South Korea In this case study, ECMWF showed best prediction skill for the spatio-temporal distribution of snow depth. At first, ECMWF EPS has been consistently enhancing the indications present in ensemble mean snow depth forecasts from 7-day lead time. Secondly, its ensemble probabilities in excess of 2~5 cm/6 hour have been coincided with observation frequencies. And this snowfall case could be predicted from 5-day lead time by using 10-day lag ensemble mean 6 hr accumulated snow depth distribution. In addition, the cause of good performances at ECMWF EPS in predicted snow depth amounts was due to outstanding prediction ability of forming inversion layer with below $0^{\circ}C$ temperature in low level (below 850 hPa) according to $35^{\circ}N$ at 1-day lead time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1925-1938
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• 2001

In this paper, a new block-matching algorithm for standard video encoder is presented. The slice competition method is proposed as a new scheme, as opposed to a coarse-to-fine approach. The order of calculating the SAD(Sum of Absolute Difference) to fad the best matching block is changed from a raster order to a dispersed one. Based on this scheme, the increasing SAD curve during its calculation is more linear than that of other curves. Then, the candidates of low probability can be removed in the early stage of calculation. And new MV prediction technique with an adaptive search range scheme also assists the proposed block-matching algorithm. As a result, an average of 13% improvement in computational power is recorded by only the proposed MV prediction technique. Synthetically, the computational power is reduced by 3977∼77% than that of the conventional BMAs. The average MAD is always low in various sequences. The results are also very close to the MAD of the full search block-matching algorithm.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.120-124
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• 2009

기후변화로 인한 사회, 경제, 자원, 환경, 수자원 등에 영향분석은 세계적인 연구 트렌드로 자리 잡고 있다. 다양한 모형들이 기후변화 영향을 효과적으로 평가하기 위해서 개발되고 있으나 주로 강우-유출 모형을 통한 유출의 변화 특성을 모의하는데 대부분의 연구가 초점을 맞추고 있다. 그러나 기본적으로 사용되는 강수량자료의 정확한 추정이 기후변화 연구에서 가장 중요하다고 해도 과언이 아니다. 이러한 관점에서 GCM 기후모형으로부터 유도된 기후변화 시나리오로부터 여러 단계로 가공하여 모형의 입력 자료로 사용하기 위한 강수량 자료를 생산하게 된다. 이러한 과정을 총칭해서 Downscaling이라고 한다. 본 연구에서는 기후모형으로 얻은 정보를 유역단위의 수문시나리오로 변환하기 위한 통계학적 Downscaling의 연구이론 변천 상황을 종합적으로 검토하고 각 모형이 갖는 장단점을 분석하고자 한다. 즉, Weather Generator, Single-site Nonstationary Markov Chain, Multi-site Nonstationary Markov Chain, Multi-site Weather State Based Markov Model 등 다양한 모델의 변화 및 진보 과정을 살펴보고 실제 국내 유역에 적용하여 모형의 타당성을 평가해보고자 한다. 이를 위해 IPCC 기후변화 시나리오를 활용하였으며 일강수량자료계열의 특성치, 극치수문량 변동특성 등 기후변화에 따른 영향분석을 일부 실시하여 분석하였다.