• Journal of the Korean Society for Marine Environment & Energy
• /
• v.13 no.4
• /
• pp.279-287
• /
• 2010

To identify characteristics of the water quality at Goseong bay, we analyzed long term monitoring data collected at surface and bottom water which was accumulated during the period of 1987~2009. The result showed that the waters of Goseong bay represented mesotrophic level that is based on chlorophyll-a, DIP and DIN and seasonal average of COD that indicates level by the COD criteria. This analysis can be translated that the waters is comparatively clean even though the waters is in the closed bay that slowly diffuses influx mass. We also did the time series analysis, correlation analysis and regression analysis on the moving average of the water parameter at Goseong bay. According to the results, DIP showed a increasing trend as time passed while DIN was on a decreasing trend under the same condition. In the waters of Goseong bay, the phyto-plankton growth was shown to be limited by DIN concentration. The chlrophyll-a was at the peak in August, at $4.60{\mu}g/L$. As the seasonal average and index were the highest in November, it was understood that the balance of nutrient at Goseong bay was dependent more on inner factors, ie, mass farming of aquatic species and release of bottom sediment rather than on inflow of fresh water. Accordingly, it is needed to consider the balance of nutrient like DIP and DIN to manage the water quality or estuaries at Goseong bay.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.26 no.2
• /
• pp.186-194
• /
• 2020

In this study, spectral analysis was conducted to identify environmental factors af ecting short-term changes in water temperature in the East, West and South coasts of Korea. The data used in the spectrum analysis is the 2016 summer water temperature, air temperature, tide level and wind data provided by Korea Hydrographic & Oceanographic Agency. In power spectrum results, peaks of water temperature and tide level were observed at same periods in West Sea (Incheon, Pyeungteak, Gunsan and Mokpo) and South Sea (Wando, Goheung, Yeosu, Tongyeong and Masan) where mean tidal range was more than 100 cm. On the other hand, periodicity of water temperature did not appear in East Sea and Busan where the mean tidal range was small. Coherence analysis showed that water temperature was highly correlated with tide in West Sea and three stations(Wando, Goheung and Tongyeong) of South Sea. Especially, correlation between water temperature and tide level in Wando and Tongyeong presented 0.96 at semi-diurnal period. Water temperature in Yeosu seems to have influenced by tide and inflow of fresh water. In Masan, water temperature is influenced by south wind, tide and inflow of fresh water. In East Sea, influence of tide on water temperature is small due to current and small tidal range. As a result of comparing the time series graph, stations where the correlation between water temperature and tide is high show that relatively cold water was inputted at flood tide and flow out at ebb tide. short-term variation of water temperature was affected by tide, but long-term variation over a month was affected by air temperature.

• Korean Journal of Remote Sensing
• /
• v.34 no.2_1
• /
• pp.267-282
• /
• 2018

Despite the continuous development of phenology detection studies using satellite imagery, verification through comparison with the field observed data is insufficient. Especially, in the case of Korean forests patching in various forms, it is difficult to estimate the start of season (SOS) by using only satellite images due to resolution difference. To improve the accuracy of vegetation phenology estimation, this study reconstructed the large scaled forest type map (1:5,000) with MODIS pixel resolution and produced time series vegetation phenology curves from Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from MODIS images. Based on the field observed data, extraction methods for the vegetation indices and SOS for Korean forests were compared and evaluated. We also analyzed the correlation between the composition ratio of forest types in each pixel and phenology extraction from the vegetation indices. When we compared NDVI and EVI with the field observed SOS data from the Korea National Arboretum, EVI was more accurate for Korean forests, and the first derivative was most suitable for extracting SOS in the phenology curve from the vegetation index. When the eight pixels neighboring the pixels of 7 broadleaved trees with field SOS data (center pixel) were compared to field SOS, the forest types of the best pixels with the highest correlation with the field data were deciduous forest by 67.9%, coniferous forest by 14.3%, and mixed forest by 7.7%, and the mean coefficient of determination ($R^2$) was 0.64. The average national SOS extracted from MODIS EVI were DOY 112.9 in 2014 at the earliest and DOY 129.1 in 2010 at the latest, which is about 0.16 days faster since 2003. In future research, it is necessary to expand the analysis of deciduous and mixed forests' SOS into the extraction of coniferous forest's SOS in order to understand the various climate and geomorphic factors. As such, comprehensive study should be carried out considering the diversity of forest ecosystems in Korea.

• Journal of the Korean Geographical Society
• /
• v.28 no.3
• /
• pp.193-199
• /
• 1993

The interannual fluctuation, trends and perio-dicties in summer rainfall of South Korea were analyzed primarily by using Mann-Kendall rank method and Power Spectrum analysis for the period from 1920 to 1985. Their relations to Indian summer monsoon rainfall have also been examined. Increasing or decreasing rainfall tendencies are not found in South Korea. In Power Spectrum alalysis, 2.5 years periods are predominent at the 95 per cent confidence level in south Korea as a whole and Pusan. Also the period of 11.0 years is found in Seou. There are another prominent spectral peaks at 2.4, 3.1, 6.2 and 7.3 years period, which are significant at 90 per cent confidence level.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.2
• /
• pp.289-299
• /
• 2015

The objective of the present study is to develop statistical quantitative forecast model for PM10 concentration over Seoul. We used three types of data (weather observation data in Korea, the China's weather observation data collected by GTS, and air quality numerical model forecasts). To apply the daily forecast system, hourly data are converted to daily data and then lagging was performed. The potential predictors were selected based on correlation analysis and multicollinearity check. Model validation has been performed for checking model stability. We applied two models (multiple regression model and threshold regression model) separately. The two models were compared based on the scatter plot of forecasts and observations, time series plots, RMSE, skill scores. As a result, a threshold regression model performs better than multiple regression model in high PM10 concentration cases.

• Journal of KIISE:Information Networking
• /
• v.36 no.3
• /
• pp.173-183
• /
• 2009

We present a real-time monitoring system for detecting anomalous network events using the entropy. The entropy accounts for the effects of disorder in the system. When an abnormal factor arises to agitate the current system the entropy must show an abrupt change. In this paper we deliberately model the Internet to measure the entropy. Packets flowing between these two networks may incur to sustain the current value. In the proposed system we keep track of the value of entropy in time to pinpoint the sudden changes in the value. The time-series data of entropy are transformed into the two-dimensional domains to help visually inspect the activities on the network. We examine the system using network traffic traces containing notorious worms and DoS attacks on the testbed. Furthermore, we compare our proposed system of time series forecasting method, such as EWMA, holt-winters, and PCA in terms of sensitive. The result suggests that our approach be able to detect anomalies with the fairly high accuracy. Our contributions are two folds: (1) highly sensitive detection of anomalies and (2) visualization of network activities to alert anomalies.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.271-271
• /
• 2023

수문학적 기준지점(HRS, Reference Hydrological Station)은 유량의 변동성의 장기적인 추세를 파악하기 위해 고품질의 자료를 생산하는 관측소를 의미한다. 선진외국의 경우 운영목적과 수문학적 기준지점의 정의는 조금씩 다르지만 유사한 개념의 관측소를 운영하고 있다. 호주의 경우 기후변화에 따른 장기간의 수자원 부존량의 변화를 예측하기 위한 모니터링 지점으로 정의하며, 미국의 경우 시간에 따른 수문학적 특성의 자연적인 변화 및 인간의 활동에 따른 수문환경의 변화에 대한 연구를 위한 기준값을 제공하기 위해 참조지점(HBN, Hydrological Benchmark Network)의 자료를 제공하기 위해 운영한다. 영국은 기후변화에 따른 유역의 수문학적 응답을 조사할 목적으로 참조지점(RHN, Reference Hydrological Network)을 운영하고 있으며, 주로 자연유역에 설치하여 운영하고 있다. WMO는 2006년 '기후연구를 위한 적절한 유량관측소'를 선정해 줄 것을 회원국에 요청하고, 관련 자료의 데이터베이스를 독일의 GRDC(Global Runoff Data Centre)에 수집하고 있다. 국외의 경우 '자연에 가까운 유역특성을 갖는 하천 유량관측망 중 양질의 자료를 보유하고 있는 관측소'를 고려하여 수문학적 기준지점을 선정한다. 하지만 우리나라의 경우 장기간의 유량자료를 보유하고 있는 관측소가 상대적으로 부족하고, 장기간의 유량자료를 보유한 지점 또한 홍수예보, 댐 운영 등 물관리 업무에 직접 활용하기 위해 대하천의 본류 중심으로 자료를 생산하고 있다. 따라서 현재를 기준으로 국제적으로 통용되는 기준에 부합하는 기준관측소를 선정하는 것은 곤란한 상황으로 미래에 수문학적 기준지점이 될 수 있는 관측소를 선정하여 장기간 모니터링을 통해 기준관측소를 확대해 나갈 필요가 있다. 본 연구에서는 국외의 수문학적 기준관측소 선정기준을 비교 검토하여 우리나라 실정에 맞는 기준관측소 선정기준을 개발하였다. 선정 기준은 ① 유역의 개발정도, ② 댐·저수지 등 인위적인 조절 정도, ③ 취수량 또는 방류량 등 유역간의 물 이동, ④ 유량자료의 보유기간 및 정확도 등을 고려하여 기준을 설정하였다. 또한 기준지점의 선정을 위한 절차를 ① 수위관측소 사전목록의 작성, ② 관측소 정보 분석(유역특성, 시계열자료 등), ③ 수문학적 기준관측소 후보 선정, ④ 유관기관 및 전문가 검토를 통한 우선순위 선정 등 4단계로 구분하여 제시하였다.

• Journal of Korea Water Resources Association
• /
• v.45 no.4
• /
• pp.393-407
• /
• 2012

This study suggests the results of temporal and spatial variations for rainfall data in the Korean Peninsula. We got the index of the rainfall amount, frequency and extreme indices from 65 weather stations. The results could be easily understood by drawing the graph, and the Mann-Kendall trend analysis was also used to determine the tendency (up & downward/no trend) of rainfall and temperature where the trend could not be clear. Moreover, by using the FARD, frequency probability rainfalls could be calculated for 100 and 200 years and then compared each other value through the moment method, maximum likelihood method and probability weighted moments. The Average Rainfall Index (ARI) which is meant comprehensive rainfalls risk for the flood could be obtained from calculating an arithmetic mean of the RI for Amount (RIA), RI for Extreme (RIE), and RI for Frequency (RIF) and as well as the characteristics of rainfalls have been mainly classified into Amount, Extremes, and Frequency. As a result, these each Average Rainfall Indices could be increased respectively into 22.3%, 26.2%, and 5.1% for a recent decade. Since this study showed the recent climate change trend in detail, it will be useful data for the research of climate change adaptation.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.1
• /
• pp.644-653
• /
• 2018

Landslides occur frequently due to the effects of heavy rainfall and typhoons caused by climate change. Erosion control measures are needed to effectively prevent landslide damage. In order to improve their efficiency, it is necessary to quantitatively measure the sediment discharge from the mountain stream. In this study, a load cell sensor was installed in a mountain stream and the measured values were compared according to the applicability and load test type in the mountain stream. The result of the load test showed that the effect of the loading type (load test 1, 2) was low at average (loadings) of 0.4kgf and 0.6kgf at sites 1 and 2, respectively. The load factor was also derived by regression analysis to increase the accuracy of the measured values. According to the results of the load factor (normalized) to the load-cell measurement value, the output value increased by 14.8% and 24.6% in sites 1 and 2, respectively, and was calculated to be similar to the reference value. The load cell sensor enabled us to quantitatively estimate the amount of sediment discharge in the mountain stream through time series analysis with the water level and rainfall information. If the monitoring is carried out for a long time, it can be used to find the sediment discharge mechanism for the mountain stream. In addition, applying sensors such as load-cells to a mountain stream is expected to contribute to the development of related industries, such as the manufacturing of measurement sensors.

• Journal of Digital Convergence
• /
• v.18 no.11
• /
• pp.259-266
• /
• 2020

Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction.