• 한국환경보건학회지
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• 제44권6호
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• pp.539-547
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• 2018

Objectives: Ozone ($O_3$) advisories are issued by provincial/prefectural and city governments in Korea and Japan when oxidant concentrations exceed the criteria of the related country. Advisories issued only after exposure to high $O_3$ concentrations cannot be considered ideal measures. Forecasts of $O_3$ would be more beneficial to citizens' health and daily life than real-time advisories. The present study was undertaken to present a simplified forecasting model that can predict surface $O_3$ concentrations for the afternoon of the day of the forecast. Methods: For the construction of a simple and practical model, a multivariate regression model was applied. The monitored data on gases and climate variables from Japan's air quality networks that were recorded over nearly one year starting from April 2016 were applied as the subject for our model. Results: A well-known inverse correlation between $NO_2$ and $O_3$ was confirmed by the monitored data for Iksan, Korea and Fukuoka, Japan. Typical time fluctuations for $O_3$ and $NO_x$ were also found. Our model suggests that insolation is the most influential factor in determining the concentration of $O_3$. $CH_4$ also plays a major role in our model. It was possible to visually check for the fit of a theoretical distribution to the observed data by examining the probability-probability (P-P) scatter plot. The goodness of fit of the model in this study was also successfully validated through a comparison (r=0.8, p<0.05) of the measured and predicted $O_3$ concentrations. Conclusions: The advantage of our model is that it is capable of immediate forecasting of surface $O_3$ for the afternoon of the day from the routinely measured values of the precursor and meteorological parameters. Although a comparison to other approaches for $O_3$ forecasting was not carried out, the model suggested in this study would be very helpful for the citizens of Korea and Japan, especially during the $O_3$ season from May to June.

• 한국대기환경학회지
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• 제15권6호
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• pp.779-789
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• 1999

To support daily ground-level $O_3$ forecasting in Seoul, a transfer function model(TFM) has been developed by using surface meteorological data and pollutant data(previous-day [$O_3$] and [$NO_2$]) from 1 May to 31 August in 1997. The forecast performance of the TFM was evaluated by statistical comparison with $O_3$ concentration observed during September it is shown that correlation coefficient(R), root mean squared error(RMSE), normalized mean squared error(NMSE) and mean relative error(MRE) were 0.73, 15.64, 0.006 and 0.101, respectively. The TFM appeared to have some difficulty forecasting very high $O_3$ concentrations. To compare with this model, multiple regression model(MRM) was developed for the same period. According to statistical comparison between the TFM and MRM. two models had similar predictive capability but TFM based on $O_3$ concentration higher than 60 ppb provided more accurate forecast than MRM. It was concluded that statistical model based on TFM can be useful for improving the accuracy of local $O_3$ forecast.

• 전기학회논문지
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• 제66권3호
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• pp.493-499
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• 2017

This paper proposes a combined very-short-term and short-term multi-variate time-series model for 24 hour load forecasting. First, the best model for very-short-term and short-term load forecasting is selected by considering the least error value, and then they are combined by the optimal forecasting time. The actual load data of industry complex is used to show the effectiveness of the proposed model. As a result the load forecasting accuracy of the combined model has increased more than a single model for 24 hour load forecasting.

• 한국대기환경학회지
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• 제23권1호
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• pp.14-28
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• 2007

This study presented the selection of ozone ($O_3$) potential factors and designed and assessed its potential prediction model using multiple-linear regression equations in Ulsan area during the springtime from April to June, $2000{\sim}2004$. $O_3$ potential factors were selected by analyzing the relationship between meterological parameters and surface $O_3$ concentrations. In addition, cluster analysis (e.g., average linkage and K-means clustering techniques) was performed to identify three major synoptic patterns (e.g., $P1{\sim}P3$) for an $O_3$ potential prediction model. P1 is characterized by a presence of a low-pressure system over northeastern Korea, the Ulsan was influenced by the northwesterly synoptic flow leading to a retarded sea breeze development. P2 is characterized by a weakening high-pressure system over Korea, and P3 is clearly associated with a migratory anticyclone. The stepwise linear regression was performed to develop models for prediction of the highest 1-h $O_3$ occurring in the Ulsan. The results of the models were rather satisfactory, and the high $O_3$ simulation accuracy for $P1{\sim}P3$ synoptic patterns was found to be 79, 85, and 95%, respectively ($2000{\sim}2004$). The $O_3$ potential prediction model for $P1{\sim}P3$ using the predicted meteorological data in 2005 showed good high $O_3$ prediction performance with 78, 75, and 70%, respectively. Therefore the regression models can be a useful tool for forecasting of local $O_3$ concentration.

• 한국대기환경학회:학술대회논문집
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• 한국대기환경학회 2000년도 추계학술대회 논문집
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• pp.37-39
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• 2000

오존에 대한 예보 모델을 연구하는 데는 오존의 생성과 소멸에 관한 광 화학 반응에 대한 이해가 중요한 데 대류권에서 일어나는 알짜 오존 생성(net ozone production)반응은 다음과 같다. (R1) $HO_2$.+NO$\longrightarrow$$NO_2$+OH. (R2) $RO_2$.+NO$\longrightarrow$$NO_2$+RO. (R3) $NO_2$+hu(424< nm) $\longrightarrow$NO+O($^{3}P$) (R4) O($^{3}P$)+$O_2$+M$\longrightarrow$$O_3$+M이때 (R1)과 (R2) 반응에 참여하는 $HO_2$.라디칼 / $RO_2$.라디칼은 주로 대기 중에 존재하는 탄화수소(RH)와 OH.의 반응에 의하여 직접 생성되기도 하고, 이때 생성된 알데하이드(RCHO) 화합물이 OH.과의 반응과 광분해 반응을 통해서 형성된다. 한편, 대도시 지역의 경우 자동차의 배기가스가 알데하이드 화합물의 주요 인위적인 배출원으로 알려져 있다(Viskari et al., 2000, Granby et al., 1997). (중략)

• 산업경영시스템학회지
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• 제46권3호
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• pp.7-14
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• 2023

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

• 한국도로학회논문집
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• 제18권3호
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• pp.47-57
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• 2016

PURPOSES : This study aims to examine the differences between the existing traffic demand forecasting method and the traffic demand forecasting method considering future regional development plans and new road construction and expansion plans using a four-step traffic demand forecast for a more objective and sophisticated national highway maintenance. This study ultimately aims to present future pavement deterioration and budget forecasting planning based on the examination. METHODS : This study used the latest data offered by the Korea Transport Data Base (KTDB) as the basic data for demand forecast. The analysis scope was set using the Daejeon Metropolitan City's O/D and network data. This study used a traffic demand program called TransCad, and performed a traffic assignment by vehicle type through the application of a user equilibrium-based multi-class assignment technique. This study forecasted future traffic demand by verifying whether or not a realistic traffic pattern was expressed similarly by undertaking a calibration process. This study performed a life cycle cost analysis based on traffic using the forecasted future demand or existing past pattern, or by assuming the constant traffic demand. The maintenance criteria were decided according to equivalent single axle loads (ESAL). The maintenance period in the concerned section was calculated in this study. This study also computed the maintenance costs using a construction method by applying the maintenance criteria considering the ESAL. The road user costs were calculated by using the user cost calculation logic applied to the Korean Pavement Management System, which is the existing study outcome. RESULTS : This study ascertained that the increase and decrease of traffic occurred in the concerned section according to the future development plans. Furthermore, there were differences from demand forecasting that did not consider the development plans. Realistic and accurate demand forecasting supported an optimized decision making that efficiently assigns maintenance costs, and can be used as very important basic information for maintenance decision making. CONCLUSIONS : Therefore, decision making for a more efficient and sophisticated road management than the method assuming future traffic can be expected to be the same as the existing pattern or steady traffic demand. The reflection of a reliable forecasting of the future traffic demand to life cycle cost analysis (LCCA) can be a very vital factor because many studies are generally performed without considering the future traffic demand or with an analysis through setting a scenario upon LCCA within a pavement management system.

• 대한원격탐사학회지
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• 제37권3호
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• pp.657-664
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• 2021

본 연구에서는 수도권 지역의 대기오염물질(PM_2.5, PM₁₀, O₃) 농도와 지형 고도, 건물 면적비, 인구 밀도의 상관성을 조사하였다. 지형 고도와 건물 면적비를 분석하기 위해 국토지리정보원에서 제공하는 수치지형도를 이용하였고, 건물 면적비를 계산하기 위해 수도권 지역을 TM 중부원점을 기준으로 수평 9 km × 9 km 격자로 구분하였다. 인구 밀도는 국가통계포털의 행정구역별 면적과 인구수 자료를 이용하였다. 대기오염물질 농도 자료는 수도권에 위치한 도시대기측정소 146개 지점의 PM_2.5, PM₁₀, O₃ 농도 측정 자료를 이용하였다. 분석 기간은 2010년 1월부터 2020년 12월까지이고, 1시간 평균 농도 자료를 이용하여 월평균 농도를 계산하였다. 지형 고도는 경기도 북부와 동부 지역에서 높았고 서해안에 근접할수록 낮았다. 건물 면적비와 인구밀도 분포는 서로 유사하였고, 서울특별시에서 가장 높았으며, 산악과 해안지역에서는 낮게 나타났다. 월평균 PM_2.5과 PM₁₀ 농도는 봄철과 겨울철(1월~3월)에 높았고 O₃ 농도는 늦봄부터 초여름(4~6월)까지 높았다. 농도가 높은 3개월에 대해서 AMQS 지점별 평균 농도를 비교·분석하였다. 건물면적비나 인구밀도와 대기오염물질 농도 사이에는 음의 상관 관계가 분석되었다(인구밀도와 PM_2.5, PM₁₀ 농도 사이는 약한 음의 상관관계가, O₃ 농도와는 비교적 강한 음의 상관관계). 반면, 대기오염물질 농도와 도시대기측정소 측정 고도 사이의 뚜렷한 상관성을 나타나지 않았는데, 향후, 이에 대한 연구 수행이 필요할 것으로 판단된다.

• 한국항만경제학회지
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• 제26권1호
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• pp.83-104
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• 2010

본 연구의 목적은 환동해권 교역 거점으로서 동해항 및 속초항의 컨테이너화물 물동량을 예측함으로써 컨테이너화물의 수급정책의 제시 및 지역경제의 활성화에 기여하는 데 있다. 본 연구의 방법론으로는 관련 문헌의 서베이 및 국제컨테이너물동량의 O/D조사에 기초한 물동량 추정방법론이 적용되었다. 컨테이너화물의 O/D자료로는 한국해양수산개발원의 "해상물동량 O/D자료를 활용한 컨테이너물동량 추정"자료가 활용되었다. 또한 내륙컨테이너기지의 컨테이너화물 예측을 위해서는 일본 서안물동량, 중국 동북3성 물동량 및 극동 러시아물동량에 대해서 심층적으로 분석하였다. 결론으로는 첫째, 강원도의 컨테이너항만정책으로서 FEU당 10만원의 인센티브 수준이 5만원 수준보다 더 바람직한 것으로 나타났다. 둘재, 동해항 및 속초항만의 컨테이너 물동량은 2008년 6,653TEU에서 2010년 22,388TEU, 2015년 152,367TEU, 2020년 354,217TEU로 추정되었다. 세째, 동해항 및 속초항은 자동차로 1시간 이내 거리이므로 공동 항만마케팅이 요구된다.

• 통상정보연구
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• 제18권1호
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• pp.81-105
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• 2016

최근 중국 동북 3성과 극동 러시아지역의 개발전략은 현 정부의 유라시아 이니셔티브 국가전략과 맞물려 그 중요성이 배가되고 있는 실정이다. 자루비노항 등을 유라시아 물류네트워크의 허브로 적극 활용해야 한다. 본 연구는 현실적 변화가 뚜렷할 중국 동북 3성을 포함하여 우리나라의 투자가 병행되고 있는 러시아 극동지역, 기타 몽골지역을 대상 존으로 삼고 자루비노항을 경유 가능한 물동량 수요 여부와 예상 품목 등을 전망하였다. 현지의 관련정보 등의 부재를 극복하기 위하여 우리나라의 기존 정보와 원단위를 활용하였다. 이는 파일롯 연구로써 동북 3성 전역을 대신하여 지린성의 산업단지시설에 국한하여 이를 도출하였다. 전통적인 교통 4단계 수요추정방법을 근간으로 운송 분야에 적합하게 수정, 보완한 방법론을 제시하였다. 본 연구는 유라시아 동북지역 물류에 대한 인식제고와 함께 정부가 의지를 가지고 추진하고 있는 해당지역 물류정책에 시사점을 제시하는데 기여한 측면이 있다고 판단된다.