• Journal of the Korean Data and Information Science Society
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• 제25권4호
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• pp.807-817
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• 2014

본 논문은 최근 많은 관심을 받는 미세먼지 (PM10)의 일별 평균농도에 대해서 전국 16개 시도에서 2008년부터 2011년까지 관측한 다변량 시계열 자료에 대한 연구이다. 다변량 시계열 모형을 이용해서 시간 및 공간에 대한 상관관계를 동시에 고려, 일변량 혹은 특정 지역에 국한해서 분석한 기존의 연구와 차별성을 두었다. 또한 Davis 등 (2013)이 제안한 부분 스펙트럼 일관성 (partial spectral coherence)을 통해 다른 지역간의 상호 의존성을 파악하고 이를 토대로 변수 선택을 통해 희박벡터자기회귀모형 (sVAR; sparse vector autoregressive model)을 적합하는 방법론을 적용하여 고차원 자료 분석의 단점 및 한계를 보완하였으며 예측력 비교를 통해서 sVAR 모형 적합의 타당성을 검증하였다.

• Ocean and Polar Research
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• 제34권2호
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• pp.185-195
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• 2012

This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

• Journal of Astronomy and Space Sciences
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• 제35권3호
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• pp.175-183
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• 2018

Many recent satellites have mission periods longer than 10 years; thus, satellite-based local space weather monitoring is becoming more important than ever. This article describes the instruments and data applications of the Korea Space wEather Monitor (KSEM), which is a space weather payload of the GeoKompsat-2A (GK-2A) geostationary satellite. The KSEM payload consists of energetic particle detectors, magnetometers, and a satellite charging monitor. KSEM will provide accurate measurements of the energetic particle flux and three-axis magnetic field, which are the most essential elements of space weather events, and use sensors and external data such as GOES and DSCOVR to provide five essential space weather products. The longitude of GK-2A is $128.2^{\circ}E$, while those of the GOES satellite series are $75^{\circ}W$ and $135^{\circ}W$. Multi-satellite measurements of a wide distribution of geostationary equatorial orbits by KSEM/GK-2A and other satellites will enable the development, improvement, and verification of new space weather forecasting models. KSEM employs a service-oriented magnetometer designed by ESA to reduce magnetic noise from the satellite in real time with a very short boom (1 m), which demonstrates that a satellite-based magnetometer can be made simpler and more convenient without losing any performance.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.127-135
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• 2009

한국의 경제규모가 꾸준히 커감에 따라 가정, 건물, 공장 등에서 필요로 하는 전력량이 지속적으로 증가하고 있다. 전력공급의 안정화를 위해서는 최대전력량보다 전력공급능력이 높아야 한다. 월별 최대전력량을 잘 설명할 수 있는 통계모형을 찾기 위해 Winters 모형, 분해 시계열모형, ARMA 모형, 설명 변수를 통해 추세성분과 계절성분을 교정한 모형을 살펴보았다. 모형의 예측력 비교 기준으로 모형적합으로부터 구한 RMSE와 MAPE가 사용되었다. 여름철 최대전력량을 예측하기 위해 평균기온과 열대야 일수를 설명 변수로 갖는 시계열 모형이 가장 우수하였다. 아울러 외부요인을 갖는 극단분포 모형을 이용한 분석을 시도하였다.

• 한국환경농학회지
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• 제33권1호
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• pp.52-56
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• 2014

BACKGROUND: It is known that impacts of climate change on damage occurrence by insect pests and diseases are increasing. The negative effects of climate change on production will threaten our food security. It is needed that on the basis of analysis of the impacts, proper strategies in response to climate change are developed. METHODS AND RESULTS: The objective of this paper is to estimate impacts of climate change on rice damage occurrence by insect pests and diseases, using the panal model which analyzes both cross-section data and time series data. The result of an analysis on impacts of climate change on rice damage occurrence by pest insect and disease showed that the damage occurrence by Rice leaf roller and Rice water weevil increased if temperature increased, and damage occurrence by Stripe, Sheath blight, and Leaf Blast increased if precipitation(or amount of sunshine) increased(or decreased). CONCLUSION: Adaptation strategies, supplying weather forecasting information by region, developing systematical strategies for prevention of damage occurrence by pest insect and disease, analyzing the factors of damage occurrence by unexpected pest insect and disease, enforcing international cooperation for prevention of damage occurrence are needed to minimize the impacts of damage occurrence on rice production.

• 한국시스템다이내믹스연구
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• 제11권4호
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• pp.117-142
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• 2010

National territorial policies require a series of dynamic simulations, which would facilitate effectiveness measuring and forecasting works geared towards territorial policies under consideration or implementation. This paper aims at designing an integrated prototype for the proposed territorial policies. After the simulation exercises for the Ochang Industrial Complex(OIC) in Chungbuk Province, this study firstly finds meaningful mismatch phenomena between housing and population increases as the in-migration time lag seems inevitable even after the housing construction is in a mature state. Secondly, the OIC development exerts more significant impact on the number of employees than that of business units. Thirdly, in- and out-migration orders are different during the first and second stages of OIC development. That is, Chungbuk Province records the largest in terms of in-migration volume, followed by the Capital and Non-Capital Regions. Even though Chungbuk Province ranks the top position in the out-migration volume, the rank of the Capital and Non-Capital Regions is reversed: the our-migration volume towards the Non-Capital Region outruns that of the Capital Region.

• 한국농림기상학회지
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• 제6권4호
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• pp.242-249
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• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2000년도 춘계 학술대회 논문집 통권 3호 Proceedings of the 2000 KSRS Spring Meeting
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• pp.49-54
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• 2000

Conventional methods for measuring winds provide wind velocity observations over limited area and time period. The use of satellite imagery for measuring wind velocity overcomes some of these limitations by providing wide area and near condinuous coverage. And its accurate depiction is essential for operational weather forecasting and for initialization of NWP models. GMS-5 provides full disk images at hourly intervals. At four times each day - 0500, 1100, 1700, 2300 hours UTC-a series of three images is received, separated by thirty minutes, centered at the four times. The current wind system generates winds from sets of 3 infrared(IR) images, separated by an hour, four times a day. It also produces visible(VIS) and water vapor(WV) image-based winds from half-hourly imagery four times a day. The derivation of wind from satellite imagery involves the identification of suitable cloud targets. tracking the targets on sequential images, associating a pressure height with the derived wind vector, and quality control. The aim of this research is to incorporate imagery from other available spectral channels and examine the error characteristics of winds derived from these images.

• Communications for Statistical Applications and Methods
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• 제23권3호
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• pp.203-213
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• 2016

We investigate volatility spillover aspects of realized volatilities (RVs) for the log returns of the Korea Composite Stock Price Index (KOSPI) and the Hang Seng Index (HSI) from 2009-2013. For all RVs, significant long memories and asymmetries are identified. For a model selection, we consider three commonly used time series models as well as three models that incorporate long memory and asymmetry. Taking into account of goodness-of-fit and forecasting ability, Leverage heteroskedastic autoregressive realized volatility (LHAR) model is selected for the given data. The LHAR model finds significant decompositions of the spillover effect from the HSI to the KOSPI into moderate negative daily spillover, positive weekly spillover and positive monthly spillover, and from the KOSPI to the HSI into substantial negative weekly spillover and positive monthly spillover. An interesting result from the analysis is that the daily volatility spillover from the HSI to the KOSPI is significant versus the insignificant daily volatility spillover of the KOSPI to HSI. The daily volatility in Hong Kong affects next day volatility in Korea but the daily volatility in Korea does not affect next day volatility in Hong Kong.

• 응용통계연구
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• 제32권2호
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• pp.319-330
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• 2019

과거의 전력소모량을 분석하여 미래의 전력소모량을 예측하는 것은 에너지 계획과 정책 결정에 있어 많은 이점을 가져다준다. 기계학습은 최근 전력소모량을 예측하는 분석 방법으로 많이 사용하고 있다. 그중 앙상블 학습은 모형의 과적합 현상을 방지하고 분산을 줄여 예측의 정확성을 높이는 방법으로 알려져 있다. 하지만 일별 데이터에 앙상블 학습을 적용했을 때 분석 방법의 특성으로 인해 피크를 잘 나타내지 못하고 중심값으로 예측하는 단점을 보였다. 본 연구에서는 앙상블 학습 전에 온도 변수와의 상관성을 고려하여 선형모형으로 적합함으로써 앙상블 학습의 단점을 보완한다. 그리고 9개의 모형을 비교한 결과 온도 변수를 선형모형으로 적합하고 랜덤포레스트를 사용한 모형이 결과가 가장 좋음을 보여준다.