A5022667248- Hydrology and Watershed Management Studies
- Hydrological Forecasting Using AI
- Climate variability and models
- Water Quality Monitoring and Analysis
- Geochemistry and Geologic Mapping
- Air Quality Monitoring and Forecasting
- Water Quality Monitoring Technologies
- Marine and coastal ecosystems
- Meteorological Phenomena and Simulations
- Groundwater and Isotope Geochemistry
- Soil Geostatistics and Mapping
- Soil erosion and sediment transport
- Odor and Emission Control Technologies
- Soil and Water Nutrient Dynamics
Korea University
2024-2025
University of Seoul
2022-2024
Accurately predicting the distribution of soil organic carbon (SOC) is essential for sustainable land management and climate change mitigation. However, due to significant spatial variability SOC complex interactions among factors, precise prediction remains a challenging task. With advancements in remote sensing technologies increased data availability, various types have been utilized prediction. Nevertheless, traditional machine learning models often rely on single-modal data, which...
The increasing complexity of water pollution and its impact on aquatic ecosystems necessitates the accurate prediction pollutant loads for effective river management. Total Organic Carbon (TOC), a key indicator organic levels, is central to assessing ecosystem health informing treatment strategies. However, conventional process-based modeling methods, while capable providing precise quality predictions, require extensive input data significant computational resources, limiting their...
Abstract The hydrologic connectivity of non-floodplain wetlands ( NFWs ) with downstream water DW has gained increased importance, but via groundwater GW is largely unknown owing to the high complexity hydrological processes and climatic seasonality. In this study, a causal inference method, convergent cross mapping (CCM), was applied detect causality between upland NFW through . CCM nonlinear method for detecting relationships among environmental variables weak or moderate coupling in...
Abstract Machine learning models (MLMs) are increasingly used with remotely sensed data to monitor chlorophyll-a (Chl-a). MLMs require large amounts of Chl-a effectively. However, weather conditions, satellite revisit cycles, and coverage constraints can impede the collection adequate data. To address this, we tested whether effectively improved predictions concentrations within 16 lakes Nakdong River in South Korea using two datasets (Sentinel-2 Landsat-8). This study evaluated four MLMs:...
Objectives : This study aims to assess the applicability of SWAT-C water quality model recently developed predict in-stream Total Organic Carbon (TOC) in a watershed within South Korea.Methods The was tested Hwangryong River Watershed. is an advanced version Soil and Water Assessment Tool (SWAT) simulate carbon cycling at scale. simulated for 11 years (2010-2020) consisting 2-year warm-up (2010-2011), 6-year calibration (2012-2017), 3-year validation (2018-2020) periods. calibrated validated...
Abstract Long-term trends in groundwater levels over the contiguous United States (CONUS) have been investigated for water resource management. Numerical modeling or remotely sensed data are frequently utilized as analytical tools; however, observational on CONUS rarely used. This study explored at 642 wells across from 1990 to 2019. Wells with less than 30% of missing 30 years were selected, and their assessed using a Seasonal Mann-Kendall’s test Sen’s slope. Trends three climatic variables...