박사과정 정혜민 (구두발표)

• Predicting TOC load in river systems using deep learning and water quality modeling concepts

박사과정 이병원 (구두발표)

• Enhancement of hydrologic model optimization with single-step reinforcement learning

박사과정 이영훈 (포스터 발표)

• Application of multi-modal deep learning framework for predicting the distribution of soil organic carbon

석사과정 김민창 (구두발표)

• Integrated Water Quality Analysis in the Anseongcheon Watershed: A SWAT-EFDC Hybrid Modeling Approach

• Enhancing Water Quality Prediction Using the SWAT-GAT (Graph Attention Networks) Hybrid Model

석사과정 이윤노 (구두발표)

• Mapping flood inundation under climate change with coupled SWAT and HEC-RAS model

• Prediction of Mosquito Population Distribution Using Artificial Intelligence

석사과정 박신범 (포스터발표)

• Application of Transfer Learning to predict in-stream TOC in Han River, South Korea

• Prediction of Entrained Flow Gasifier Performance via Machine Learning Models

박사과정 정혜민 (포스터발표)

• Predicting the distribution of soil heavy metals across abandoned mining sites using machine learning models

• Improving prediction capacity of a hybrid model of LSTM and SWAT by reducing parameter uncertainty

석사과정 이병원 (포스터발표)

• Benefits of multiplt remotely sensed datasets and machine learning models to predict the Chlorophyll-a concentration

• Assessing the parameter uncertainty of SWAT-C using vegetation constraints

석사과정 이영훈 (포스터발표)

• Evaluating the spatial distribution of NO3-N concentration in groundwater across Jeju island, South Korea, utilizing machine learning models combined with geospatial data

• Machine learning applications for predicting soil organic carbon and redistribution rates in agricultural landscapes: A case study of lowa

박사과정 정혜민 (포스터발표)

• Improving the prediction capacity of a hybrid method of SWAT and LSTM by reducing parameter uncertainty

석사과정 김동호 (포스터발표)

• Improving the prediction capacity of a hybrid method of SWAT and LSTM by reducing parameter uncertainty

석사과정 권용성 (포스터발표)

• Assessing the impacts of dam and weir operation on streamflow predictions using LSTM across South Korea

석사과정 이병원 (포스터발표)

• Use of multiple sensors and machine learning models to predict the spatial distribution of chlorophyll-a