ScholarWorks@Gyeongsang National University: Ki, Seo Jin

Detailed Information

Ki, Seo Jin (기서진)

건설환경공과대학 (환경공학과)

Research Area

Tel 0557723341
Homepage

ResearcherID ORCiD Scopus Google Scholar ID ResearchGate

Keyword

Climate change:5||Deep learning:5||SWAT:4||기계학습:4||Machine learning:4||Selforganizing map:3||Water quality:3||Long shortterm memory:3||Longitudinal connectivity:3||심층학습:3||종적연결성:2||Temporal resolution:2||Convolutional neural network:2||.:2||Seawater reverse osmosis (SWRO):2||BSM1:2||Invasive fish:2||Ecological health assessment:2||LabVIEW:2||장단기 메모리:2||Largemouth bass:2||Liquid phase plasma:2||Degradation:1||Degradation efficiency:1||Degradation pathway:1||Degradation rate constant:1||Diethyl phthalate:1||Diffuse pollution:1||Dimensionality reduction:1||Distribution shift:1||Energy efficiency:1||Engineered system:1||Ensemble learning:1||Environmental impact:1||Evapotranspiration:1||Excel macros:1||Extended Kalman filter (EKF):1||Feasibility assessment:1||Fish:1||Fish assemblage:1||Fish fauna:1||Freshwater:1||Future weather scenarios:1||GIS:1||Gated recurrent unit:1||Greenhouse cultivation effluents:1||Habitat suitability index:1||Hybrid model:1||Hydraulic parameters:1||Hydrogen:1||Hydrogen peroxide:1||Hydrothermal energy:1||Hyperparameter tuning:1||Immobilized photocatalyst:1||Important variables:1||Incomplete data:1||Influencing factors:1||Internally staged design (ISD):1||Interpolation methods:1||Invasive species:1||Kalman filter:1||Logistic regression:1||Machine Learning:1||Management priority ranking:1||Membrane maintenance:1||Microbial electrolysis cell:1||Minimum temperature:1||Missing values:1||Multi layer perceptron:1||Multilayer stacking:1||Multiobjective optimization:1||Multivariate data:1||Multivariate time series:1||Nakdong River:1||Natural system:1||Noise reduction:1||Nonlinear data analysis:1||Nonpoint sources:1||Operating conditions:1||Operating parameters:1||Peak detection:1||Pipe failure event:1||Pollution contribution:1||Pollution hotspots:1||Pollution loads:1||Prediction accuracy:1||Prediction models:1||Predictive performance:1||Priority ranking:1||Private company relocation measure:1||Process performance:1||Public and private buildings:1||Pumping and drainage facilities:1||Random forests:1||Recurrent neural network:1||Regional Climate Model:1||Remote sensing:1||Restoration:1||Reverse osmosis:1||Risk assessment:1||River monitoring data:1||River water quality:1||Seasonal variation of seawater:1||Sequential prediction model:1||Sewer systems:1||Species distribution model:1||Streamflow:1||Substrate concentration:1||Supervised learning algorithms:1||Support vector machine:1||Temporal Resolution:1||Theoretical potential:1||TiO2 photocatalyst:1||Time series:1||Time series data:1||Titanium dioxide:1||Tolerance guilds:1||Total water usage:1||Tree pruning:1||Trophic guilds:1||Tungsten oxide:1||Types of relocation:1||Univariate data:1||Unscented Kalman filter (UKF):1||Upper Nam River Basin:1||Variable importance:1||Variance inflation factor:1||Wastewater treatment process":1||Water quality characteristics of groundwater:1||Water quality data:1||Water quantity:1||Watershed management plans:1||Weather variables:1||Weir:1||activated carbon powder:1||air temperature forecasting:1||artificial barrier:1||artificial neural network:1||attenuation capacity:1||biomonitoring:1||caret package:1||conventional survey:1||deep learning:1||eDNA metabarcoding:1||emerging hot spot analysis:1||ensemble model:1||freshwater fish community:1||gross primary productivity:1||groundwater vulnerability:1||invasive fish:1||iron oxide nanoparticle:1||land surface temperature:1||leaf area index:1||liquid phase plasma:1||longitudinal connectivity:1||multivariate data set:1||nitrogendoped carbon:1||overlayindex model:1||pseudocapacitive characteristics:1||review:1||seasonal variation:1||sensible and latent heat fluxes:1||species distribution model:1||species diversity:1||surface water quality:1||univariate data set:1||variational data assimilation:1||결측치:1||군집생태학:1||기업이전대책:1||기업이전유형:1||기질 농도:1||내성도길드:1||다변수 시계열:1||다중 레이어 스태킹:1||데이터 재표본화:1||랜덤포레스트:1||미생물전기분해전지:1||변수 중요도:1||보:1||보간 방법:1||복원:1||상관관계:1||생물학적 건전성:1||생태건강성 평가:1||생태학적 건강성 평가:1||섭식길드:1||수소:1||수질:1||앙상블 학습:1||어류 군집:1||어류상:1||에너지 효율:1||예측 성능:1||인공구조물:1||자기조직화지도:1||전이학습:1||종적 연결성:1||지하수 수질 특성:1||초매개변수 조정:1||카렛패키지:1||폐수처리공정:1||하천유량:1||"Transfer learning:1||합성곱 신경망:1||1,4Dichlorobenzene:1||ARIMA:1||Agricultural nonpoint sources:1||Artificial neural network:1||Artificial structure:1||Best management practices:1||Biological integrity:1||Bioprocess:1||Caret package:1||Classification algorithm:1||Cleaning frequency:1||Cloudy day reconstruction:1||Combined processes:1||Community ecology:1||Cooling and heating energy:1||Correlation:1||Customized model:1||Data Resampling:1||Decision tree:1||Deep Learning:1||Deep learning architecture:1||

STATISTICS: Total View :6,346,030; Today View :8,756

RSS_1.0 RSS_2.0 ATOM_1.0

Gyeongsang National University Central Library, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do, 52828, Republic of Korea+82-55-772-0533

Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.

Detailed Information

Keyword

BROWSE