• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1747-1761
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• 2023

Wetlands, defined as lands periodically inundated or exposed during the year, are crucial for sustaining biodiversity and filtering environmental pollutants. The importance of mapping and monitoring their topographical changes is therefore paramount. This study focuses on the topographical variations at the Incheon River estuary wetland post-restoration, noting a lack of adequate prior measurements. Using a multi-temporal Sentinel-1 dataset from October 2014 to March 2023, we mapped long-term variations in water bodies and detected topographical change anomalies using a pixel-frequency approach. Our analysis, based on 196 Sentinel-1 acquisitions from an ascending orbit, revealed significant topography changes. Since 2020, employing the pixel-frequency technique, we observed area increases of +0.0195, 0.0016, 0.0075, and 0.0163 km² in water level sections at depths of 2-3 m, 1-2 m, 0-1 m, and less than 0 m, respectively. These findings underscore the effectiveness of the wetland restoration efforts in the area.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.92-94
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• 1997

By measuring the increase of regional cerebral blood flow (rCBF) during the activation tasks, we can describe the brain regions that participate in certain specific functions. In this study, we composed the functional maps of verbal and nonverbal memory by performing the rCBF positron emission tomography (PET) activation studies and analyzing the differences between control and each activation state. Successive four tasks, which consist of one control state and three different activation tasks, were performed on 6 normal volunteers. All images were spatially normalized on standard atlas and the differences between control and activation states were statistically analyzed. The verbal memory activated predominantly left-sided structures, especially left superior temporal cortex, and the nonverbal short-term memory activated the right frontal cortex. Also, some regions ,where is thought to be related with short-term memory system, such as cingulate gyrus and hippocampus were activated. We conclude that biological validity of the brain regions for verbal and nonverbal memory could be tested using rCBF PET imaging technique and statistical analysis.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.3
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• pp.133-140
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• 2002

Site-specific minimum temperature forecasts are critical in a short-term decision making procedure for preventive measures as well as a long-term strategy such as site selection in fruits industry. Nocturnal cold air pools frequently termed in mountainous areas under anticyclonic systems are very dangerous to the flowering buds in spring over Korea, but the spatial resolution to detect them exceeds the current weather forecast scale. To supplement the insufficient spatial resolution of official forecasts, we developed a GIS - assisted frost risk assesment scheme for using in mountainous areas. Daily minimum temperature data were obtained from 6 sites located in a 2.1 by 2.1 km area with complex topography near the southern edge of Sobaek mountains during radiative cooling nights in spring 2001. A digital elevation model with a 10 m spatial resolution was prepared for the entire study area and the cold air inflow was simulated for each grid cell by counting the number of surrounding cells coming into the processing cell. Primitive temperature surfaces were prepared for the corresponding dates by interpolating the Korea Meteorological Administration's automated observational data with the lapse rate correction. The cell temperature values corresponding to the 6 observation sites were extracted from the primitive temperature surface, and subtracted from the observed values to obtain the estimation error. The errors were regressed to the flow accumulation at the corresponding cells, delineating a statistically significant relationship. When we applied this relationship to the primitive temperature surfaces of frost nights during April 2002, there was a good agreement with the observations, showing a feasibility of site-specific frost warning system development in mountainous areas.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.54.4-55
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• 2021

The Seoul National University AGN Monitoring Project (SAMP) is a welldesigned long-term AGN reverberation mapping project. SAMP focuses on the luminous AGNs out to z~0.5 with relative long time lags between AGN continuum and broad emission lines and aims to probe the high-end of the AGN broad line region (BLR) size-luminosity (R-L) relation. The pilot observations started in October 2015 for 100 AGNs to confirm the variability and the H and [O III] emission line strengths. Based on the initial variability test, 48 quasars has been continued spectroscopic monitoring since Feb. 2016 with Lick 3m and MDM 2.4m telescopes with a cadence of ~20 days. Supporting photometric monitoring in B and V band was conducted at multiple facilities including the MDM 1.3m, LOAO, and DOAO telescopes with a cadence of ~10 days. By the time of Feb. 2021, we have obtained five years spectroscopic and photometric data. More than 30 AGNs shows significant variability in five-year baseline and 16 of them show well detected lags between B-band and H. Here, we report some examples of SAMP light curves and lag detections using the first five-year data as well as the location of our 16 targets in the AGN BLR R-L relation. These measurements are consistent with the existing R-L relation and located at the high-end. With the coming data, SAMP are hopefully to report more AGNs with well detected lags. Our results demonstrate the general feasibility and potential of long-term reverberation project with medium cadence for luminous AGNs.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.287-301
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• 2024

Purpose: This paper aims to verify the difference in production resilience between local clusters and regions without clusters before and after a major crisis. Furthermore, this paper aims to identify the clusters' quality factors that impact clusters' shock vulnerability and resilience. Methods: Utilizing open-source data from the US Cluster Mapping platform, this paper compares regions with industrial clusters to those without using the Differences-in-Differences (DID) estimator. It considers the regions with industrial clusters as a treatment group and others as the control group, comparing the period before and after the pandemic. Additionally, the paper examines which cluster factors make a difference in economic resilience during the crisis using Regression Discontinuity Design (RDD). Results: The study finds that regions with industrial clusters show higher production resilience compared to regions without clusters. Moreover, the number of establishments, annual payrolls, and employment can have a positive impact on resilience during the pandemic shock. Conclusion: Though clusters could be vulnerable during the global crisis, industrial clusters can contribute to regional economic development and production resilience in the long-term aspect. Thus, it is required to construct a high-quality local cluster and support it during the economic crisis in the long-term aspect.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.419-431
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• 2021

In Seoul, it has been confirmed that the duration of rainfall is shortened and the frequency and intensity of heavy rains are increasing with a changing climate. In addition, due to high population density and urbanization in most areas, floods frequently occur in flood-prone areas for the increase in impermeable areas. Furthermore, the Seoul City is pursuing various projects such as structural and non-structural measures to resolve flood-prone areas. A disaster prevention performance target was set in consideration of the climate change impact of future precipitation, and this study conducted to reduce the overall flood damage in Seoul for the long-term. In this study, 29 GCMs with RCP4.5 and RCP8.5 scenarios were used for spatial and temporal disaggregation, and we also considered for 3 research periods, which is short-term (2006-2040, P1), mid-term (2041-2070, P2), and long-term (2071-2100, P3), respectively. For spatial downscaling, daily data of GCM was processed through Quantile Mapping based on the rainfall of the Seoul station managed by the Korea Meteorological Administration and for temporal downscaling, daily data were downscaled to hourly data through k-nearest neighbor resampling and nonparametric temporal detailing techniques using genetic algorithms. Through temporal downscaling, 100 detailed scenarios were calculated for each GCM scenario, and the IDF curve was calculated based on a total of 2,900 detailed scenarios, and by averaging this, the change in the future extreme rainfall was calculated. As a result, it was confirmed that the probability of rainfall for a duration of 100 years and a duration of 1 hour increased by 8 to 16% in the RCP4.5 scenario, and increased by 7 to 26% in the RCP8.5 scenario. Based on the results of this study, the amount of rainfall designed to prepare for future climate change in Seoul was estimated and if can be used to establish purpose-wise water related disaster prevention policies.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.133-138
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• 2002

In this paper, we propose an integrated GUI environment of parallel fuzzy inference system fur pattern classification of remote sensing data. In this, as 4 fuzzy variables in condition part and 104 fuzzy rules are used, a real time and parallel approach is required. For frost fuzzy computation, we use the scan line conversion algorithm to convert lines of each fuzzy linguistic term to the closest integer pixels. We design 4 fuzzy processor unit to be operated in parallel by using FPGA. As a GUI environment, PCI transmission, image data pre-processing, integer pixel mapping and fuzzy membership tuning are considered. This system can be used in a pattern classification system requiring a rapid inference time in a real-time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.