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물벼룩과 형광성 박테리아를 이용한 중금속의 급성독성평가
하헌중,김성태,최종욱,민선홍,장태연,김건흥 ( Hern Jung Ha,Sung Tae Kim,Jong Uk Choi,Seon Hong Min,Tae Yeon Jang,Geon Heung Kim ) 한국하천호수학회 1995 생태와 환경 Vol.28 No.3
It is very difficult to estimate the hazardous effects of toxicants because they interact each other in additive, synergistic, and antagonistic ways. Bioassay is an approaching method for toxicity evaluation of actual or potential danger in hazardous wastes. This study is to evaluate the susceptibility of 3 water fleas and luminescent bacteria Photobacterium Phosphoreum to heavy metals. The toxcity test is performed against 7 heavy metals. For water fleas, Hg shows the highest toxicity and Fe shows the lowest toxicity by the 24 or 48 hour LC_(50). For luminescent bacteria, by the 5min EC_(50) Hg is more toxic than any other metals. M. macrosopa and D. psittacea have a good reproductivity in the laboratory and show the possibility as an aquatic toxicity test organism.
남동호(Nam, Dong Ho),하헌중(Ha, Hern Joong),김병식(Kim, Byung Sik) 한국방재학회 2020 한국방재학회논문집 Vol.20 No.1
Due toclimate change, the average temperature of the Earth continues to increase, while abnormal climate patterns (such as El Niño and La Niña) occur frequently, causing numerous instances of flooding and drought damages. Thus, sophisticated analyses of rainfall-runoff phenomena are needed to reduce the damage caused by these weather disasters. Furthermore, analyzing the impact of extreme rainfall events occurring in a short period of time is essential for flood management. In this study, the Nakdong River, located in Yangsan, Gyeongsangnam-do, which is prone to localized heavy rainfall and flash floods, was selected as the target basin to conduct flood-runoff simulation. We used distributed runoff models such as spatial runoff assessment tool (S-RAT) and Vflo™ for this simulation, and compared and analyzed their results. Furthermore, using the same events, the validity and applicability of the S-RAT model has been verified through calibration. The errors of both models were calculated using statistical analysis to examine the domestic basin applicability of the S-RAT model. 전 세계적으로 기후변화로 인하여 지구의 평균기온이 상승하고 엘리뇨, 라니냐와 같은 이상기후가 빈번하게 발생하며 이로인한 홍수나 가뭄 등의 피해 또한 잦아지고 있다. 이러한 기상재해에 따른 피해를 줄이기 위해 강우-유출 현상에 관한 정교한해석이 필요하며, 단기간에 발생되는 호우사상에 대한 유출해석은 홍수관리 측면에서 중요한 역할을 한다. 본 논문에서는국지성 집중호우 및 돌발홍수가 잦은 낙동강수계인 경상남도 양산에 위치한 양산천을 대상유역으로 선정하여 홍수유출모의를실시하였다. 홍수유출모의에는 분포형모형인 S-RAT모형과 Vflo™모형을 사용하였다. 또한 S-RAT모형의 검증을 위해 동일한사상을 이용하여 보정 및 검증을 하였으며, 통계학적 분석을 통하여 두 모형의 오차를 계산하여 S-RAT모형의 적용성을 검토하였다.
Extreme learning machine 기법을 이용한 소양강댐 월 유입량 예측
김병식(ByungSik Kim),최승철(SeungCheol Choi),이병현(ByungHyun Lee),하헌중(HernJoong Ha) 한국데이터정보과학회 2024 한국데이터정보과학회지 Vol.35 No.3
In recent years, the frequency of flooding due to heavy rainfall has been increasing due to climate change, highlighting the growing importance of disaster prevention. Accurate prediction of dam inflow is crucial during heavy rainfall and typhoons for proper dam discharge. To simulate inflow, various approaches, including physical models and machine learning models., are employed. In this study, we utilized the Extreme Learning Machine (ELM), a machine learning technique, to simulate the inflow of Soyang River Dam watershed using precipitation data observed at the weather station in Inje (Station 211) and historical inflow data. Data were collected from January 1974 to August 2023, with training using data from January 1974 to December 2020 and validation with data from January 2021 to August 2023. Additionally, we compared the results of ELM with those of a Multilayer Perceptron (MLP) with a similar structure and conducted model validation using evaluation metrics. The proposed ELM model showed validation results for the test data, achieving an MAE of 19.98, MSE of 931.25 and R-squared value of 0.83.