http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : 김교식 ( Kyosik Kim ) (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
김교식(Kim, Kyosik),김병현(Kim, Byunghyun),한건연(Han, Kun-Yeun) 한국방재학회 2021 한국방재학회논문집 Vol.21 No.2
많은 연구자들이 가뭄예측을 높이는 연구를 지속적으로 이루어지고 있으나, 가뭄의 빈도와 패턴이 불규칙한 시계열을 가지고 있어 예측을 하기에는 한계가 있다. 가뭄은 복잡한 시계열을 가지고 있기에 하나의 모형으로만 예측하기도 어렵고, 다수의 모형으로 예측을 수행하여도 높은 예측이 나올 수 있다고 단정하기도 어렵다. 따라서, 강우, 기온, 일조량, 풍량 등과 같은 설명변수를 입력자료로 사용하여 예측을 높이는데 많은 연구들이 이루어지고 있다. 본 연구의 목적은 기상에서 일어나는 물리적인 현상을 기후자료를 이용하여 복잡하고 어려운 시계열 가뭄지수를 하나로 나타내는 표준강수증발산지수(SPEI)를 이용하여 가뭄예측 방법을 마련하고자 한다. 표준강수증발산지수는 강수량과 증발산량 자료를 이용하여 누적강수량에서 누적증발량을 제외하여 산정하는 방법이며, 증발산량은 월열지수법을 사용하였다. 기상청은 6개월 누적강수량 기준인 SPI6을 이용하여 기상가뭄을 평가하여, 본 연구에서도 월자료 및 일자료 SPEI6을 기준하여 기계학습에 적용하여 수행하였으며, 그 결과 ANN 월자료 R2는 안동 0.488, 문경 0.533, 구미 0.594, SVR 0.452, 0.496, 0.564, RF 0.355, 0.467, 0.524이며, 일자료는 ANN 0.923, 0.919, 0.915, SVR 0.925, 0.923, 0.896, RF 0.915, 0.915, 0.797로, 모든 지점에서 일자료 SPEI를 기계학습에 적용시 높은 예측을 수행하였음을 확인하였다. There has been much research recently to improve the prediction of drought, but the frequency and pattern of drought displays an irregular time series that limits its predictability, making it difficult to predict with only a single model, and high-level predictions cannot be made even when many models are applied. Therefore, many studies have been conducted to improve predictions by using explanatory variables such as precipitation, temperature, sunshine duration, and air volume as input data. The purpose of this study is to devise a method for predicting drought using the Standard Precipitation Evaporation Index (SPEI), which represents a complex and difficult time series drought index using climate data for weather phenomena. The Standard Precipitation Evaporation Index is a method of calculating the cumulative precipitation by excluding the cumulative evaporation amount from the cumulative precipitation using precipitation and evapotranspiration data, and the evaporation amount is calculated using the monthly heat index method. The Meteorological Agency evaluated meteorological drought using SPI6, which is a 6-month cumulative precipitation standard, and applied it to machine learning based on monthly data and daily data SPEI6 in this study. As a result, ANN monthly data R2 was 0.488 in Andong and 0.533 in Mungyeong, Gumi 0.594, SVR 0.452, 0.496, 0.564, RF 0.355, 0.467, 0.524, and the daily data are ANN 0.923, 0.919, 0.915, SVR 0.925, 0.923, 0.896, RF 0.915, 0.915, 0.797, and the daily data SPEI at all points. It was confirmed that high prediction was obtained when machine learning was applied to these methods.
-
김해시 물 순환 개선 도시계획에 의한 비점오염물질 저감효과 분석
정강영 ( Kang-young Jung ),김신 ( Shin-kim ),권헌각 ( Hun-gak Kwon ),양득석 ( Duk Seok Yang ),김교식 ( Kyosik Kim ),장광진 ( Kwang-jin Jang ),신동석 ( Dong-seok Shin ),안정민 ( Jung-min Ahn ) 한국환경과학회 2016 한국환경과학회지 Vol.25 No.11
This study was performed to analyze the effects of a water circulation green area plan on non-point source pollution in Gimhae South Korea. A quantitative analysis of Arc-GIS data was conducted by applying a watershed model based on Fortran to investigate the changes to direct runoff and pollution load. Results showed that prior to the implementation of the water circulation green area plan in Gimhae, direct runoff was 444.05 m<sup>3</sup>/year, total biological oxygen demand (BOD) pollution load was 21,696 kg/year, and total phosphorus (TP) pollution load was 1,743 kg/year. Implementation of the development plan was found to reduce direct runoff by 2.27%, BOD pollution load by 1.16% and TP pollution load by 0.19% annually. The reduction in direct runoff and non-point source pollution were attributed to improvements in the design of impermeable layers within the city.
-
금호강 수계 지류하천의 수질 특성 평가 및 수질개선 등급화 방안
정강영 ( Kang Young Jung ),안정민 ( Jung Min Ahn ),김교식 ( Kyosik Kim ),이인정 ( In Jung Lee ),양득석 ( Duk Seok Yang ) 한국환경과학회 2016 한국환경과학회지 Vol.25 No.6
In this study, we analyzed on-site monitoring data for 15 tributaries in Geumho watersheds for 3 years (2011-2013) in order to sort out priorities on water quality characteristics and improvement. As a result of estimating contribution to contamination of the tributary rivers, Dalseocheon showed the highest load densities, despite the smallest watershed area, with 22.7% BOD5, 30.7% CODMn, 31.3% TOC and 47.6% TP. After conducting PCA (principal component analysis) and FA (factor analysis) to analyze water quality characteristics of the tributary rivers, the first factor was classified as CODMn, TOC, EC, TP and BOD5, the second factor as pH, Chl-a and DO, the third factor as water temperature and TN, and the fourth factor as SS and surface flow. In addition, arithmetical sum of each factor’s scores based on grading criteria revealed that Dalseocheon and Namcheon were classified into Group A for their highest scores - 96 and 93, respectively -, and selected as rivers that require water environmental management measures the most. Also, water environmental contamination inspection showed that Palgeocheon had the most number of aquatic factors to be controlled: BOD5, CODMn, SS, TOC, T-P, Chl-a, etc.
ScienceON연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
