• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2001년도 춘계 학술발표논문집
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• pp.43-48
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• 2001

Many applications in the areas of agricultural, hydrological and environmental resource management require data over very large areas and with a high imaging frequency - monitoring crop growth, water stress, seasonal wetland flooding and natural vegetation development. This precludes the use of fine resolution data (Landsat, Spot) on the grounds of cost, accessibility and low imaging frequency. Meteorological satellites have the potential to fill this need, given their very wide spatial coverage, and high repeat imaging. The Remote Sensing Unit (RSU) at the Zambia Meteorological Department routinely receives, processes and archives imagery from both Meteosat and NOAA AVHRR satellites. Here I wish to present some examples of applications of these data sets that arise from the RSU work - relationships between rainfall and vegetation development as assessed by satellite, derived information and seasonal patterns of flooding in the Barotse floodplain and the Kafue flats. I also wish to outline ways in which a more widespread use of this data by the Zambian institutions canbe achieved.

• 한국물환경학회지
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• 제34권1호
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• pp.67-76
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• 2018

There are 34 mega-cities with a population of more than 10 million in the world. One of the highly populated cities in the world is Seoul in South Korea. Seoul receives $1,140million\;m^3/year$ for domestic water, $2million\;m^3/year$ for agricultural water and $6million\;m^3/year$ for industrial water from multi-purpose dams. The maintenance water used for water conservation, ecosystem protection and landscape preservation is $158million\;m^3/year$, which is supplied from natural precipitation. Recently, the use of the other water for preservation of water quality and ecosystem protection in urban areas is increasing. The objectives of this study is to develop the seasonal forecast method of environmental water in urban areas (Seoul, Daejeon, Gwangju, Busan) and to evaluate its predictability. In order to estimate the seasonal outlook information of environmental water from Land Surface Model (LSM), we used the observation weather data of Automated Synoptic Observing System (ASOS) sites, forecast and hind cast data of GloSea5. In the past 30 years (1985 ~ 2014), precipitation, natural runoff and Urban Environmental Water Index (UEI) were analyzed in the 4 urban areas. We calculated the seasonal outlook values of the UEI based on GloSea5 for 2015 year and compared it to UEI based on observed data. The seasonal outlook of UEI in urban areas presented high predictability in the spring, autumn and winter. Studies have depicted that the proposed UEI will be useful for evaluating urban environmental water and the predictability of UEI using GloSea5 forecast data is likely to be high in the order of autumn, winter, spring and summer.

• 대한교통학회지
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• 제29권5호
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• pp.139-155
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• 2011

본 연구에서는 계절성(seasonality)을 감안한 적분된 자기회귀 이동평균 모형(ARIMA model)을 이용하여 우리나라 지역 간 철도의 동태적 변화과정을 추정하고 장래 통행수요를 예측하고자 하였다. 기존 국내연구에서 고려하지 않은 계절성 요인을 감안한 ARIMA 모형(Seasonal ARIMA model)과 월별 지역 간 철도 통행실적자료를 이용하여 교통수요 동태적 변화모형을 구축하였다. 구체적으로 2000년 1월부터 2008년 12월까지의 월별 수송인원 및 수송인-km 기준 지역 간 통행실적 자료를 이용하여 Box et al. (1994)에서 제시한 Seasonal ARIMA 모형을 적용하였으며 이에 따라 장래 지역 간 철도 통행수요를 예측하였다. 장래 통행수요 예측 결과에 따르면 수송인원 기준으로 2015년 및 2020년에는 2008년의 각각 약 1.36배와 1.71배 수준으로 산정되었다. 또한 수송인-km 기준으로 2015년과 2020년에는 2008년의 각각 약 1.25배와 1.78배 정도로 예측되었다.

• 한국항만경제학회지
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• 제29권3호
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• pp.1-23
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• 2013

본 연구는 1992년부터 2011년까지 월별자료를 사용하여 여러 가지 시계열 추정모델과 승법 계절 ARIMA 모형을 설정하여 부산항의 컨테이너 물동량을 추정하고 예측하였다. 여러 가지 모델로 추정한 결과 부산항의 컨테이너 물동량과 물동량 변동 모두 계절을 승법한 ARIMA 모델 $(1,0,1){\times}(1,0,1)_{12}$로 추정하였을 때, 추정결과와 Akaike information, Schwarz, Hannan-Quin 기준 등으로 보아, 가장 좋은 ARIMA 추정과 예측 모형으로 나타났다. 그리하여 부산항 물동량 추정의 최적모형인 ARIMA $(1,0,1){\times}(1,0,1)_{12}$ 모형에 의해 향후 8년간 96개월에 대한 부산항 물동량 미래 예측치(2013-2020년)를 월별로 추정하여 예측한 결과 2013년부터 부산의 물동량은 연도별로 조금씩 지속적으로 증가하는 추세를 보일 것으로 나타났다. ARIMA $(1,0,1){\times}(1,0,1)_{12}$ 모형에 의한 부산항의 컨테이너 물동량의 연도별 예측량은 2013년 1천 891만 TEU, 2014년 2천 34만 TEU, 2015년 2천 188만 TEU, 2016년 2천 353만 TEU, 2017년 2천 531만 TEU, 2018년 2천 722만 TEU 그리고 2020년 3천 148만 TEU 등으로 나타났다.

• 한국물환경학회지
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• 제25권4호
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• pp.539-546
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• 2009

We examined the effects of land cover types on water quality based on data surveyed during April 2007-February 2008 from 178 sites of 111 streams in Paldang watershed. BOD, COD, DO, SS, T-N, and T-P concentrations of spring and summer were strongly and significantly associated with the first principal component of the proportions of eight land cover types, and differences between all parameter's concentration except SS and T-N of spring and summer were insignificantly related with them. SS and T-N concentration of summer were significantly correlated with increase and decrease of stream flow. T-P concentration of spring was the most significantly related with the second principal component which was positively correlated with the proportions of residential and forest land covers and was negatively correlated with the proportions of paddy and grass land covers. It is necessary to manage land use of the upper watershed and stream flow for improvement in water quality because seasonal variations of each water quality parameter are dependent upon land cover and flow variations.

• 한국수자원학회논문집
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• 제32권4호
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• pp.501-510
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• 1999

북한강 수계의 농업소유역에 대하여 하천수(2년)와 지하수(1년) 수질을 모니터링하여 분석하였다. 농업소유역의 주요한 비점원 오염물질인 총질소, 질산성 질소, 총인, BOD, TSS 및 대장균 농도를 주기적으로 측정하였다. 계절에 따른 수질의 변화 및 지하수와 하천수 수질과의 관계 비교를 통하여 토지이용이 수질에 미치는 영향을 분석하였다. 연구유역의 지하수 수위와 총질소, 질산성 질소의 농도의 벼농사와 밀접한 관련이 있었고 지하수와 하천수의 질소농도의 변화도 밀접한 관련이 있음이 나타나 벼농사가 하천의 질소농도에 많은영향을 주는 것으로 나타났다. 그러나 토지이용(벼농사)과 지하수 및 하천수의 총인, BOD, 대장균 농도 사이에는 일정한 관계를 발견할 수 없었다. 본 연구결과는 농업소유역의 수질변화를 이해하고 소하천의 수질관리정책을 개발하는데 유용하게 활용될 수 있을 것이다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.1308-1314
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• 2008

In this paper, Seoulmetro that is the first operation organization which operates a city railroad rolling-stock maintenance RIMS(rolling stock information maintenance system) collected and analyzed a light maintenance data and introduced time a series analysis technique to find the way how to contribute to a use efficiency improvement of a city railroad. The purpose of time a series analysis is to remove a seasonal change including data and to check an irregular fluctuation. First of all, a collection range of the data comes under a light maintenance, however it needs a data of more than 3 years to check the seasonal change. We put a study for an accumulated scope that the data satisfy a period like this and are able to extend a range of the study when time flys forward. The data used for study is filtered using a movement average method after passing proper selection working and is solved with a method which looks for season index. Using the season index that was getten in here, we predict a light working frequency, if it has an irregular change, we will contribute it to a city railroad a use efficiency improvement and establish the cause by carrying out prevent maintenance in advance.

• 한국조경학회지
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• 제30권4호
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• pp.19-27
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• 2002

When we research a landscape or make a Landscape Impact Assessment, we use the image of a specific season like summer or fall. Since there are four distinct seasons, each with a different landscape, researchers need to understand viewers′visual preferences for individual seasonal landscapes. The purpose of this study is to investigate viewers′visual preferences according to seasonal change and the respondent′s age, gender and profession. In this research, the independent variable is season: suing, summer, fall, winter and snowy winter. Three landscape types used in the experiment: forest, street and agriculture. Each landscape type has two sites for reliability. The assessment media for this research are pictures featuring landscapes taken in each of the four seasons. The study used the "paired comparison" method for taking the score of visual preference. The results of this study are as follows: 1. The summer landscape has the highest visual preference score. However, spring and fall landscapes should also be considered for visual landscape evaluation. 2. The visual preference of winter landscape covered with snow is very high, but since snow is temporal and irregular, it is difficult to consider this factor for visual landscape evaluation. 3. The visual preference score of winter is the lowest of four seasons. The attractive factors of spring are flowers, summer is greenery and fall is autumnal tints. But these are not present in winter. 4. The result of visual preferences according to age groups, gender and profession have no serious differences. 5. Visual preference to scenery of 4 seasons by age group was not different from general preference and thus was concluded to have no connection with age. 6. As a result from the research of visual preference to scenery of 4 seasons by sex, women were shown to like snow-scene more than men. This study presents an indication of general preferences of seasonal landscapes. It is expected that more advanced study will proceed after this one.

• 한국측량학회지
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• 제35권4호
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• pp.249-260
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• 2017

UHI (Urban Heat Island) is an important environmental issue occurring in highly developed (or urbanized) area such as Seoul Metropolitan City of Korea due to modification of the land surface by man-made structures. With the advance of the remote sensing technique, land cover types and LST (Land Surface Temperature) influencing UHI were frequently investigated describing that they have a positive relationship. However, the concept of land cover considers material characteristics of the urban cover in a comprehensive way and does not provide information on how human activities influence on LST in detail. Instead, land use reflects ways of land use management and human life patterns and behaviors, and explains the relationship with human activities in more details. Using this concept, LST was segmented according to land use types from the Landsat imagery to identify the human-induced heat from the surface and interannual and seasonal variation of LST with GIS. The result showed that the LST intensity of Seoul was greatest in the industrial area and followed by the commercial and residential areas. In terms of size, the residential area could be defined as the major contributor among six urban land use types (i.e., residential, industrial, commercial, transportation, etc.) affecting UHI during daytime in Seoul. For temperature, the industrial area was highest and could be defined as a major contributor. It was found that land use type was more appropriate to understand the human-induced effect on LST rather than land cover. Also, there was no significant change in the interannual pattern of LST in Seoul but the seasonal difference provided a trigger that the human life pattern could be identified from the satellite-derived LST.