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인공지능 모델에 의한 지하수위 모의결과의 적절성 판단을 위한 허용가능한 예측오차 범위의 추정
신문주,문수형,문덕철,류호윤,강경구,Shin, Mun-Ju,Moon, Soo-Hyoung,Moon, Duk-Chul,Ryu, Ho-Yoon,Kang, Kyung Goo 한국수자원학회 2021 한국수자원학회논문집 Vol.54 No.7
Groundwater is an important water resource that can be used along with surface water. In particular, in the case of island regions, research on groundwater level variability is essential for stable groundwater use because the ratio of groundwater use is relatively high. Researches using artificial intelligence models (AIs) for the prediction and analysis of groundwater level variability are continuously increasing. However, there are insufficient studies presenting evaluation criteria to judge the appropriateness of groundwater level prediction. This study comprehensively analyzed the research results that predicted the groundwater level using AIs for various regions around the world over the past 20 years to present the range of allowable groundwater level prediction errors. As a result, the groundwater level prediction error increased as the observed groundwater level variability increased. Therefore, the criteria for evaluating the adequacy of the groundwater level prediction by an AI is presented as follows: less than or equal to the root mean square error or maximum error calculated using the linear regression equations presented in this study, or NSE ≥ 0.849 or R<sup>2</sup> ≥ 0.880. This allowable prediction error range can be used as a reference for determining the appropriateness of the groundwater level prediction using an AI.
신문주(Munju Shin),표영덕(Youngdug Pyo),박정권(Jungkwon Park),정수진(Soojin Jeong),이충원(Choongwon Lee) 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
A study of power and emission in a CRDI diesel engine by using Dimethyl ether(DME) is an oxygenated fuel with a cetane number higher than that of diesel oil. It meet the ULEV emission regulation and reduce the smoke to almost zero when used in a diesel engine. Also, Test results showed that the torque and power and brake thermal efficiency with DME were same as those of pure diesel oil, The test results showed that DOC was the vary effective method to reduce the CO emission in case of Dimethyl ehter fuelin diesel engine. but, THC emission is showed a little reduction rates than CO reduction rates.also EGR system was the very effective method to reduce the NOx emission in case of DME fuel in diesel engine.
SWAT모형을 이용한 용담댐 유역의 유량 전망 결과 비교 연구
정차미,신문주,김영오,Jung, Cha Mi,Shin, Mun-Ju,Kim, Young-Oh 한국수자원학회 2015 한국수자원학회논문집 Vol.48 No.6
In this study, reliable future runoff projections based on RCPs for Yongdam dam watershed was performed using SWAT model, which was validated by k-fold cross validation method, and investigated the factors that cause the differences with respect to runoff projections between this study and previous studies. As a result, annual average runoff compared to baseline runoff would increase 17.7% and 26.1% in 2040s and 2080s respectively under RCP8.5 scenario, and 21.9% and 44.6% in 2040s and 2080s respectively under RCP4.5 scenario. Comparing the results to previous studies, minimum and maximum differences between runoff projections over different studies were 10.3% and 53.2%, even though runoff was projected by the same rainfall-runoff model. SWAT model has 27 parameters and physically based complex structure, so it tends to make different results by the model users' setting. In the future, it is necessary to reduce the cause of difference to generate standard runoff scenarios. 본 연구에서는 SWAT 모형을 이용해 용담댐 유역을 대상으로 k-fold cross validation 기법을 사용하여 신뢰성 있는 RCP 기반의 미래 유출량을 산정하고 이를 과거 연구와 비교하여 SWAT 모형을 이용한 기후변화 유량 전망 결과의 차이의 요인에 대해 살펴보았다. 그 결과, 총유출량은 baseline 대비 2040s, 2080s 기간에 RCP8.5 시나리오에서는 17.7%, 26.1% 증가, RCP4.5 시나리오의 경우에는 21.9%, 44.6% 증가할 것으로 전망되었다. 이를 선행 연구와 비교해 본 결과 같은 모형을 사용했음에도 불구하고 유량 전망치의 경우 연구결과 간 최저 10.3%에서 최대 53.2% 차이를 보였다. SWAT 모형에는 물리적 기반 모형으로 27개의 많은 매개변수가 존재하고 사용자마다 모형을 구축하는 과정에서 차이가 많이 발생할 수 있다. 향후 이러한 차이요인을 저감하여 표준화된 유량시나리오 생성을 위한 노력이 필요하다.